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Adapting speaker recognition systems to new environments is a widely-used technique to improve a well-performing model learned from large-scale data towards a task-specific small-scale data scenarios. However, previous studies focus on single domain adaptation, which neglects a more practical scenario where training data are collected from multiple acoustic domains needed in forensic scenarios. Audio analysis for forensic speaker recognition offers unique challenges in model training with multi-domain training data due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and performance loss. Fine-tuning is a commonly-used method for adaptation in order to retrain the model with weights initialized from a well-trained model. Alternatively, in this study, three novel adaptation methods based on domain adversarial training, discrepancy minimization, and moment-matching approaches are proposed to further promote adaptation performance across multiple acoustic domains. A comprehensive set of experiments are conducted to demonstrate that: 1) diverse acoustic environments do impact speaker recognition performance, which could advance research in audio forensics, 2) domain adversarial training learns the discriminative features which are also invariant to shifts between domains, 3) discrepancy-minimizing adaptation achieves effective performance simultaneously across multiple acoustic domains, and 4) moment-matching adaptation along with dynamic distribution alignment also significantly promotes speaker recognition performance on each domain, especially for the LENA-field domain with noise compared to all other systems., Comment: IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING

Las medidas de oído real ha permitido al audioprotesista disponer de un criterio para la valoración de la adaptación de audífonos fiable y válido. El uso de estas medidas en la estimación de la bondad de la adaptación nos permite, entre otras ventajas, tener en cuenta las diferencias individuales al facilitarnos parámetros referidos al rendimiento del audífono en oído real. En el presente artículo llevaremos a cabo una revisión de las principales medidas que pueden registrarse con audioanalizador y sonda microfónica en oído real y sus principales aplicaciones.

Issues that are increasingly arising in criminal proceedings are highlighted, namely: use of data obtained as a result of diagnostic researches on video, sound recordings that allows to ensure a higher level of completeness, objectivity and comprehensiveness of research. The purpose of the article is to analyze the possibilities of forensic diagnostic researches on video and sound recordings of both oral speech and the sound environment of an offense event. Various modern possibilities of researches on video and sound recordings are considered. Attention is paid both to the specifics of diagnostic speech researches in order to obtain information about personality of the unknown speaker and to information that can be obtained through research and diagnosis of non-speech information. Analyzed the possibilities (within the framework of the examination of video, sound recording) diagnostic researches on directly speech information, including the definition of: oral speech form, nature of relations between the interlocutors, conversation meaning (definition of the meaningful situation: consent vs disagreement; permission vs prohibition; understanding vs misunderstanding; request; advice; promise; assurance; gratitude; threat; clarification; order; question; message, etc.), rate of speech and the emotional state of the interlocutors, as well as expert diagnostics of biological parameters of the speakers. Possibilities of diagnostic studies of technical studies of recording media and recording equipment, that can contain information about the technology of obtaining/fixing/saving video, sound recordings, properties and features of the media itself are considered. Non-speech sounds are classified according to their belonging to certain sources. The article highlights effectiveness of using the forensic information obtained through forensic examination in practice of investigating crimes.

The concept and essence of distortion are considered from a technical stand-point and criminal law perspective. The most common ways of distorting a voice and a speech are provided, as well as certain methods of detecting an intentional change in a voice and human speech by computer tools and linguistic analysis. Some software and hardware tools changing a speech signal both in real time and in a pre-prepared recording are analyzed. To solve diagnostic and identification tasks, a pressing issue in forensic video and audio analysis is studied which is addressed to forensic experts in this field more often.

Style transfer is one of the most popular uses of neural networks. It has been thoroughly researched, such as extracting the style from famous paintings and applying it to other images thus creating synthetic paintings. Generative adversarial networks (GANs) are used to achieve this. This paper explores the many ways in which the same results can be achieved with audio related tasks, for which a plethora of new applications can be found. Analysis of different techniques used to transfer styles of audios, specifically changing the gender of the audio is implemented. The Crowd sourced high-quality UK and Ireland English Dialect speech data set was used. In this paper, the input is the male or female wave form and the opposite gender’s waveform is synthesized by the network, with the content spoken remaining the same. Different architectures are explored, from naive techniques and directly training audio waveforms against convolution neural networks (CNN) to using extensive algorithms researched for image style conversion and generation of spectrograms (using GANs) to be trained on CNNs. This research has a broader scope when used in converting music from one genre to another, identification of synthetic voices, curating voices for AIs based on preference etc.

Open-source software libraries have a significant impact on the development of Audio Signal Processing and Music Information Retrieval (MIR) systems. Despite the abundance of such tools, there is a lack of an extensive and easy-to-use reference library for audio feature extraction on Web clients. In this article, we present Essentia.js, an open-source JavaScript (JS) library for audio and music analysis on both web clients and JS engines. Along with the Web Audio API, it can be used for both offline and real-time audio feature extraction on web browsers. Essentia.js is modular, lightweight, and easy-to-use, deploy, maintain, and integrate into the existing plethora of JS libraries and web technologies. It is powered by a WebAssembly back end cross-compiled from the Essentia C++ library, which facilitates a JS interface to a wide range of low-level and high-level audio features, including signal processing MIR algorithms as well as pre-trained TensorFlow.js machine learning models. It also provides a higher-level JS API and add-on MIR utility modules along with extensive documentation, usage examples, and tutorials. We benchmark the proposed library on two popular web browsers and the Node.js engine, and four devices, including mobile Android and iOS, comparing it to the native performance of Essentia and the Meyda JS library. The work on Essentia.js has been partially funded by the Ministry of Science and Innovation of the Spanish Government under the grant agreement PID2019-111403GB-I00 (Musical AI).

The article deals with some problems related to the production of forensic speech and audio analysis, taking into account global digitalization of all spheres of human life. Voicechanger, technology of voice synthesis signifi cantly complicate forensic phonoscopic examinations and put before the forensic experts a new, interesting challenges. We consider the algorithm for changing the voice using modern software, as well as features of voice synthesis technologies. Some variants of studying such phonograms are proposed.

Purpose Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception. Methods In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction. Results The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%. Conclusion Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

Many audio analysis systems employ peak picking procedure to produce the final decision. A typical scheme uses a thresholding function to minimise detection errors where its form depends on the structure of the input signal. The paper covers the problem of an adaptive thresholding function estimation. Using the genetic algorithm to optimise the components of the thresholding function we have determined the level of importance of individual local statistics on the final function representation. The proposed method has been used to tune the peak detection procedure to identify the change points in an audio signal. In the result of the heuristic configuration, the best accuracy of segment boundaries have been obtained for thresholding function built on top of two local statistics of the detection function and constant value. Finally, as an example, a comparison with the state–of–the–art scheme for audio segmentation was performed.

With the fast development of computer and information technology, multimedia data has become the most important form of information media. Auditory information plays an important role in information location, this comes from the fact that it can be difficult to find useful information. Thus audio classification becomes more important in audio analysis as it prepares for content-based audio retrieval. There is quite a bit of research on the topic of audio classification methods, audio feature analysis, and extraction based on audio classification. Many works of literature extract features of audio signals based on time or Fourier transform frequency domain. The emergence of the wavelet theory provides a time-frequency analysis tool for signal analysis. Wavelet transformation is a local transformation of the signal in time and frequency which can effectively extract information from the signal, and perform multi-scale refinement analysis on functions or signals through operations such as stretching and translation instead of the traditional Fourier transformation. In the time-frequency analysis of the signal, the wavelet analysis captures the local time and frequency characters of the signal which can improve the ability of signal analysis. It can also change certain locals of the signal without affecting other aspects of it. In this paper, the frequency domain features are combined with the wavelet domain features. At the same time that the MFCC features are extracted, the discrete wavelet transform is used to extract the features of the wavelet domain. Then the statistical features are extracted for each audio example, and the SVM model is used to realize the different forms of audio classification identification.

Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session.

Η Ημιτονοειδής Μοντελοποίηση είναι μια από τις πιο ευρέως χρησιμοποιούμενες παραμετρικές μεθόδους για την επεξεργασία σήματος φωνής και ήχου. Η ακριβής εκτίμηση των ημιτονοειδών παραμέτρων (πλάτη, συχνότητες, και φάσεις) είναι ένα κρίσιμο σημείο για τη ακριβή αναπαράσταση των σημάτων που αναλύονται. Στην παρούσα εργασία, με βάση τις πρόσφατες εξελίξεις στην ημιτονοειδή ανάλυση, προτείνουμε υψηλής ανάλυσης, προσαρμόσιμα ημιτονοειδή μοντέλα για συστήματα ανάλυσης, σύνθεσης, και τροποποίησης ομιλίας. Στόχος μας είναι να προσφέρουμε συστήματα που αναπαριστούν σήματα φωνής με εξαιρετικά ακριβή και συμπαγή τρόπο.Εμπνευσμένοι από πρόσφατα προταθέντα μοντέλα, όπως το προσαρμόσιμο Σχεδον - Αρμονικό Μοντέλο (aQHM) και το προσαρμόσιμο Αρμονικό Μοντέλο (aHM), διατυπώνουμε τη θεωρία της προσαρμόσιμης Ημιτονοειδούς Μοντελοποίησης και προτείνουμε ένα μοντέλο που ονομάζεται εκτεταμένο προσαρμόσιμο Σχεδον - Αρμονικό Μοντέλο (eaQHM), το οποίο είναι ένα μη παραμετρικό μοντέλο, ικανό να προσαρμόσει τα στιγμιαία πλάτη και φάσεις των συναρτήσεων βάσης του στα τοπικά χρονικά μεταβαλλόμενα χαρακτηριστικά του σήματος της φωνής, αμβλύνοντας έτσι τη γνωστή υπόθεση της τοπικής στασιμότητας. Αποδεικνύεται ότι το eaQHM παρουσιάζει υψηλότερες επιδόσεις από το aQHM στην ανάλυση και ανασύνθεση των έμφωνων τμημάτων φωνής. Με βάση το eaQHM, ένα υβριδικό σύστημα ανάλυσης / σύνθεσης ομιλίας παρουσιάζεται (eaQHNM), μαζί με μια υβριδική έκδοση του του aHM (aHNM). Επιπλέον, παρουσιάζουμε κίνητρα για μια αναπαράσταση του σήματος της φωνής σε όλο το φάσμο και σε όλη τη διάρκεια του, χρησιμοποιώντας το eaQHM, αναπαριστώντας έτσι όλα τα μέρη του σήματος της φωνής, με υψηλής ανάλυσης AM-FM ημίτονα. Η αξιολόγηση δείχνει ότι η προσαρμοσιμότητα και η σχεδόν-αρμονικότητα είναι αρκετή για να παράξει πολύ υψηλή ποιότητα στην ανασύνθεση των άφωνων τμημάτων της φωνής. Στη συνέχεια, παρουσιάζεται το σύστημα πλήρους φάσματος ανάλυσης και σύνθεσης βασισμένο στο eaQHM, το οποίο υπερτερεί συστημάτων που θεωρούνται state-of-the-art, υβριδικά ή πλήρους ανάλυσης, στην ανάλυση και ανασύνθεση φωνής. Η υπεροχή του στην ποιότητα ανασύνθεσης επιβεβαιώθηκε με αντικειμενικές και υποκειμενικές αξιολογήσεις.Όσον αφορά τις εφαρμογές, το eaQHM και το aHM εφαρμόζονται σε μετασχηματισμούς φωνής (κλιμάκωση χρόνου και κλιμάκωση θεμελιώδους συχνότητας). Οι μετασχηματισμοί που προκύπτουν είναι υψηλής ποιότητας, ακολουθώντας πολύ απλούς κανόνες, σε σύγκριση με άλλα συστήματα state-of-the-art. Οι έννοιες της σχετικής φάσης και της καθυστέρησης σχετικής φάσης είναι ζωτικής σημασίας για την ανάπτυξη μετασχηματισμένου σήματος με χαρακτηριστικά αναλλοίωτου σχήματος, χωρίς τεχνικά ελαττώματα, και υψηλής ποιότητας. Τα αποτελέσματα δείχνουν ότι τα συστηματα βασισμένα στην αρμονικότητα προτιμούνται έναντι αυτών της σχεδόν-αρμονικότητας, λόγω της απλότητας της αναπραστάσης. Επιπλέον, το eaQHM εφαρμόζεται στο πρόβλημα της μοντελοποίησης σημάτων ήχου, και συγκεκριμένα ήχων μουσικών οργάνων. Το eaQHM αξιολογείται και σύγκρινεται με state-of-the-art συστήματα, και έχει υψηλές επιδόσεις όσον αφορά την ποιότητα επανασύνθεσης, αναπαριστωντας με επιτυχία τα στάδια της επίθεσης, της μετάβασης, και της στατικότητας ενός ήχου μουσικού οργάνου. Τέλος, μια άλλη προτεινόμενη εφαρμογή έγκειται στην ανάλυση και ταξινόμηση της εκφραστικής ομιλίας. Το eaQHM εφαρμόζεται στην ανάλυση της εκφραστικής ομιλίας, παρέχοντας τις στιγμιαίες παραμέτρους του ως χαρακτηριστικά που μπορούν να χρησιμοποιηθούν στην αναγνώριση και ταξινόμηση, βασισμένη σε διανυσματικούς κβαντιστές, εκφραστικής ομιλίας. Αν και τα ημιτονοειδή μοντέλα δεν χρησιμοποιούνται συνήθως σε τέτοιες εφαρμογές, τα αποτελέσματα είναι ελπιδοφόρα.

The focus of this research is to analyze the results of encrypting audio using various authenticated encryption algorithms implemented in the Python cryptography library for ensuring authenticity and confidentiality of the original contents. The Advanced Encryption Standard (AES) is used as the underlying cryptographic primitive in conjunction with various modes including Galois Counter Mode (GCM), Counter with Cipher Block Chaining Message Authentication Code (CCM), and Cipher Block Chaining (CBC) with Keyed-Hashing for encrypting a relatively small audio file. The resulting encrypted audio shows similarity in the variance when encrypting using AES-GCM and AES-CCM. There is a noticeable reduction in variance of the performed encodings and an increase in the amount of time it takes to encrypt and decrypt the same audio file using AES-CBC with Keyed-Hashing. In addition, the corresponding encrypted using this mode audio spans a longer duration. As a result, AES should either have GCM or CCM for an efficient and reliable authenticated encryption integration within a workflow.

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.

The effectiveness of pain management relies on the choice and the correct use of suitable pain assessment tools. In the case of newborns, some of the most common tools are human-based and observational, thus affected by subjectivity and methodological problems. Therefore, in the last years there has been an increasing interest in developing an automatic machine-based pain assessment tool. This research is a preliminary investigation towards the inclusion of a scoring system for the vocal expression of the infant into an automatic tool. To this aim we present a method to compute three correlated indicators which measure three distress-related features of the cry: duration, dysphonantion and fundamental frequency of the first cry. In particular, we propose a new method to measure the dysphonantion of the cry via spectral entropy analysis, resulting in an indicator that identifies three well separated levels of distress in the vocal expression. These levels provide a classification that is highly correlated with the human-based assessment of the cry.

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

Remote acquisition of information on ecosystem dynamics is essential for conservation management, especially for the deep ocean. Soundscape offers unique opportunities to study the behavior of soniferous marine animals and their interactions with various noise-generating activities at a fine temporal resolution. However, the retrieval of soundscape information remains challenging owing to limitations in audio analysis techniques that are effective in the face of highly variable interfering sources. This study investigated the application of a seafloor acoustic observatory as a long-term platform for observing marine ecosystem dynamics through audio source separation. A source separation model based on the assumption of source-specific periodicity was used to factorize time-frequency representations of long-duration underwater recordings. With minimal supervision, the model learned to discriminate source-specific spectral features and prove to be effective in the separation of sounds made by cetaceans, soniferous fish, and abiotic sources from the deep-water soundscapes off northeastern Taiwan. Results revealed phenological differences among the sound sources and identified diurnal and seasonal interactions between cetaceans and soniferous fish. The application of clustering to source separation results generated a database featuring the diversity of soundscapes and revealed a compositional shift in clusters of cetacean vocalizations and fish choruses during diurnal and seasonal cycles. The source separation model enables the transformation of single-channel audio into multiple channels encoding the dynamics of biophony, geophony, and anthropophony, which are essential for characterizing the community of soniferous animals, quality of acoustic habitat, and their interactions. Our results demonstrated the application of source separation could facilitate acoustic diversity assessment, which is a crucial task in soundscape-based ecosystem monitoring. Future implementation of soundscape information retrieval in long-term marine observation networks will lead to the use of soundscapes as a new tool for conservation management in an increasingly noisy ocean., Author summary Understanding the status of biodiversity and its changing patterns is crucial for conservation management. Ecological assessment in deep-sea environments is challenging owning to the limited accessibility and visibility. Nevertheless, we can listen to underwater sounds to investigate the acoustic behaviors of marine animals. This study applied machine learning techniques to analyze underwater recordings transmitted from a deep seafloor observatory off northeastern Taiwan. In particular, our computational approach does not require preexisting labels in the procedure of model building. The model can automatically learn acoustic features and effectively separate sounds of marine mammals, fishes, and shipping activities with minimal supervision. The analysis of 2.5 years of audio data revealed that the studied continental shelf environment has a high diversity of sound-producing animals, and the community structure changes with diurnal, lunar, and seasonal cycles. The applied techniques also generated a database of environmental and anthropogenic sounds, which can facilitate future investigations on how human activities affect the acoustic environment and biodiversity. Our technique is entirely based on passive acoustics, which minimizes interference with animals. It also transforms audio recordings into highly interpretable ecological data, which can be useful for tracking changes in marine biodiversity.

Speech has several distinguishing characteristic features which has remained a state-of-the-art tool for extracting valuable information from audio samples. Our aim is to develop a emotion recognition system using these speech features, which would be able to accurately and efficiently recognize emotions through audio analysis. In this article, we have employed a hybrid neural network comprising four blocks of time distributed convolutional layers followed by a layer of Long Short Term Memory to achieve the same.The audio samples for the speech dataset are collectively assembled from RAVDESS, TESS and SAVEE audio datasets and are further augmented by injecting noise. Mel Spectrograms are computed from audio samples and are used to train the neural network. We have been able to achieve a testing accuracy of about 89.26%.

Real-ear measurements has allowed the audiologist to have a criterion for assessing the adaptation of reliable and valid instruments. The use of these measures in estimating the goodness of adaptation allows us, among other advantages, to take into account individual differences by providing parameters related to the performance of the hearing aid in real ear. In this article we will carry out a review of the main measurements that can be recorded with a real ear audio analyzer and microphone probe and their main applications. Las medidas de oído real ha permitido al audioprotesista disponer de un criterio para la valoración de la adaptación de audífonos fiable y válido. El uso de estas medidas en la estimación de la bondad de la adaptación nos permite, entre otras ventajas, tener en cuenta las diferencias individuales al facilitarnos parámetros referidos al rendimiento del audífono en oído real. En el presente artículo llevaremos a cabo una revisión de las principales medidas que pueden registrarse con audioanalizador y sonda microfónica en oído real y sus principales aplicaciones.

International audience; Since 2017, the goal of the two-million song WASABI database has been to build a knowledge graph linking collected metadata (artists,discography, producers, dates, etc.) with metadata generated by the analysis of both the songs’ lyrics (topics, places, emotions, structure, etc.) and audio signal (chords, sound, etc.). It relies on natural language processing and machine learning methods for extraction, and semantic Web frameworks for representation and integration. It describes more than 2 millions commercial songs, 200K albums and 77K artists. It can be exploited by music search engines, music professionals (e.g. journalists, radio presenters, music teachers) or scientists willing to analyze popular music published since 1950. It is available under an open license, inmultiple formats and with online and open source services including aninteractive navigator, a REST API and a SPARQL endpoint.

Background: The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as of Nov. 24, 2020, as per data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not. Aims: To describe how to establish a new generation of voice/audio analysis applications to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public. Materials & Methods: The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. Results: The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate, and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody, and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. Discussion: The voice/audio application current project can be merged with a mobile App called “Aarogya Setu” by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country. Conclusion: This new method of detecting cases will decrease the workload in the covid-19 laboratory.

The automatic identification and verification of speakers through representative audio continue to gain the attention of many researchers with diverse domains of applications. Despite this diversity, the availability of classified and categorized multi-purpose Arabic audio libraries is scarce. Therefore, we introduce a large Arabic-based audio clips dataset (15810 clips) of 30 popular reciters cantillating 37 chapters from the Holy Quran. These chapters have a variable number of verses saved to different subsequent folders, where each verse is allocated one folder containing 30 audio clips for the declared reciters covering the same textual content. An additional 397 audio clips for 12 competent imitators of the top reciters are collected based on popularity and number of views/downloads to allow for cross-comparison of text, reciters, and authenticity. Based on the volume, quality, and rich diversity of this dataset we anticipate a wide range of deployments for speaker identification, in addition to setting a new direction for the structure and organization of similar large audio clips dataset.

Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the multimodal approaches are to become better in a range of fields of interest, audio analysis will have to play an integral part. Event recognition in autonomous vehicles (AVs) is such a field at a nascent stage that can especially leverage solely on audio or can be part of the multimodal approach. In this manuscript, an extensive analysis focused on the comparison of different magnitude representations of the raw audio is presented. The data on which the analysis is carried out is part of the publicly available MIVIA Audio Events dataset. Single channel Short-Time Fourier Transform (STFT), mel-scale and Mel-Frequency Cepstral Coefficients (MFCCs) spectrogram representations are used. Furthermore, aggregation methods of the aforementioned spectrogram representations are examined, the feature concatenation compared to the stacking of features as separate channels. The effect of the SNR on recognition accuracy and the generalization of the proposed methods on datasets that were both seen and not seen during training are studied and reported.

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis., Comment: 9 pages, 6 figures, 2 tables, Accepted for publication at KDD'20 (Health Day)

The field of Soundscape Ecology refers to the study of sounds produced in natural environments and how they can provide important information about the state of the environment, as well as on the potential impacts caused by changes due to external influences. The analysis and visualization of large amounts of ecological recordings, as well as the development of appropriate tools for audio analysis contitute a major challenge. Mechanisms for extracting audio features, as well as the characterization of acoustic events of interest, resulting in datasets that capture the frequency variations and the occurrence of acoustic events in the recordings, still constitute a problem due to available solutions do not prove adequate for data analysis in acoustic ecology research, involving domain-specific issues and voluminous amounts of audio records collected over long periods of time. This work aims to address problems related to the extraction of audio features, providing assistance through visualization to the selection of the most significants, that could represent the subtle variations in ecological recordings, as well as assisting specialists in the generation of annotated dtasets by the characterization of acoustic events through exploratory visualizations, and methods for detecting vessels in underwater recordings. A framework named Seecology is presented, encompassing suitable methods and tools to supporting specialists and scholars of environmental analysis. Case studies were carried out with the framework in terrestrial and underwater recordings provided by acoustic ecology researchers, by producing datasets from the custom feature extractor included in the framework, and in the case of the method developed for detecting boats in underwater recordings, a comparative study to another method was conducted to determine its accuracy, in addition to the case study to determine its effectiveness. The presented methods for extracting characteristics, characterizing acoustic events through exploratory visualization and boat detection, demonstrated their effectiveness for applications in acoustic ecology, with the framework containing the methods capable of producing multidimensional datasets without excessive computational costs, allowing the user to easily generate annotations on this data through the included visualizations. The boat detection method performed better than the one it was compared, both in speed and accuracy, being able to detect weak signals from boats even under extreme noise. A área de Ecologia de Paisagens Sonoras (Soundscape Ecology) refere-se ao estudo de sons produzidos em ambientes naturais e como eles podem fornecer informações importantes sobre o meio ambiente, bem como possíveis impactos causados por alterações devido a influências externas. A análise e visualização de grandes quantidades de gravações ecológicas, juntamente com a produção de recursos para análises constituem um grande desafio. Meios para a extração de características dos áudios, bem como a caracterização de eventos acústicos de interesse, produzindo conjuntos de dados que representem as variações de frequências e eventos acústicos capturados nas gravações, ainda consituem um problema devido às soluções disponíveis não se mostrarem adequadas para análises de dados em pesquisas de ecologia acústica, envolvendo questões específicas do domínio e quantidades volumosas de registros de áudio coletados por longos períodos de tempo. Faz-se necessário o desenvolvimento de métodos e ferramental para extrair e representar a grande quantidade dos dados produzidos a partir de estudos de ecologia. Este trabalho tem por objetivo abordar problemas relacionados à extração de características dos áudios, auxiliando na seleção das mais significativas que representem as sutis variações nas gravações ecológicas, bem como auxiliar especialistas na geração de conjuntos de dados anotados pela caracterização de eventos acústicos por meio de visualizações exploratórias, e métodos para um problema específico, que é a detecção de embarcações em gravações subaquáticas. Um arcabouço nomeado Seecology é apresentado, englobando métodos e ferramentas adequados para dar suporte aos especialistas e estudiosos de análise ambiental. Estudos de caso foram realizados com o arcabouço em gravações terrestres e subaquáticas fornecidos por pesquisadores da área, produzindo conjuntos de dados a partir do extrator de características personalizado incluso no arcabouço. No caso do método desenvolvido para detecção de barcos em gravações subaquáticas, um estudo comparativo a outro método foi conduzido para determinar sua acurácia, além do estudo de caso para determinar sua eficácia. Os métodos propostos para extração de características, caracterização de eventos acústicos por meio de visualização exploratória e detecção de barcos, demonstraram sua eficácia para aplicações em ecologia acústica, sendo o arcabouço capaz de produzir conjuntos de dados multidimensionais sem custos computacionais excessivos. Dessa forma o usuário é capaz de gerar anotações nestes dados facilmente por meio das visualizações inclusas. O método de detecção de barcos obteve desempenho superior ao que foi comparado, tanto em velocidade quanto em acurácia, sendo capaz de detectar sinais fracos de barcos mesmo sob ruído extremo.

Audio analysis is fast becoming a requirement of digital media for analysing multiple frequencies of sound at a time and also reduce the background noise.The urban sound challenge is increasing day by day and the problem is meant to introduce for audio analysis and processing in the usual classification scenario. Model is implemented on the basis of keras framework and librosalibrary. Keras is capable of running of the algorithm tensorflow. Kerascan be described as an interface rather than a standalone machine learning tools. Librosa is one of the python library for music and audio analysis. It helps us with necessary music information retrieval systems. The authorcollect the database and use the data and also use the graph for a better understanding of audio data analysis.

Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram representations of the audio. In this paper, we compare commonly used mel-spectrogram representations and evaluate model performances that can be achieved by reducing the input size in terms of both lesser amount of frequency bands and larger frame rates. We use the MagnaTagaTune dataset for comprehensive performance comparisons and then compare selected configurations on the larger Million Song Dataset. The results of this study can serve researchers and practitioners in their trade-off decision between accuracy of the models, data storage size and training and inference times., The 28th European Signal Processing Conference (EUSIPCO)

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% ($\pm$ 0.55) and 94.56% ($\pm$ 0.77), respectively, whereas the multiclass had an accuracy of 78.12% ($\pm$ 2.09). The best sensitivity was observed in the ensemble approach (96.82% $\pm$ 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% $\pm$ 3.83) and the binary (88.49% $\pm$ 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% $\pm$ 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset., Comment: Published in Computers in Biology and Medicine

International audience; Cough is a symptom in over a hundred respiratory diseases. The audio features in cough signals contain erudition about the predicament of the respiratory system. Using deep learning or signal processing, these features can be used to build an effective disease prediction system. However, cough analysis remains an area that has received scant attention from machine learning researchers. This can be attributed to several factors such as inefficient ancillary systems, high expenses in obtaining datasets, or difficulty in building classifiers. This paper categorized and reviewed the current progress on cough audio analysis for the classification of pulmonary diseases. It also explored potential future issues in research. Additionally, it proposed a model for the classification of ten serious pulmonary ailments commonly seen in Indian adolescents. The proposed model is evaluated against four existing state of the art techniques in the literature.

Forensic audio analysis for speaker verification offers unique challenges due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. The lack of real naturalistic forensic audio corpora with ground-truth speaker identity represents a major challenge in this field. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and loss in performance. Alternatively, cross-domain speaker verification for multiple acoustic environments is a challenging task which could advance research in audio forensics. In this study, we introduce a CRSS-Forensics audio dataset collected in multiple acoustic environments. We pre-train a CNN-based network using the VoxCeleb data, followed by an approach which fine-tunes part of the high-level network layers with clean speech from CRSS-Forensics. Based on this fine-tuned model, we align domain-specific distributions in the embedding space with the discrepancy loss and maximum mean discrepancy (MMD). This maintains effective performance on the clean set, while simultaneously generalizes the model to other acoustic domains. From the results, we demonstrate that diverse acoustic environments affect the speaker verification performance, and that our proposed approach of cross-domain adaptation can significantly improve the results in this scenario., Comment: To appear in INTERSPEECH 2020

Producers of Electronic Dance Music (EDM) typically spend more time creating, shaping, mixing and mastering sounds, than with aspects of composition and arrangement. They analyze the sound by close listening and by leveraging audio metering and audio analysis tools, until they successfully created the desired sound aesthetics. DJs of EDM tend to play sets of songs that meet their sound ideal. We therefore suggest using audio metering and monitoring tools from the recording studio to analyze EDM, instead of relying on conventional low-level audio features. We test our novel set of features by a simple classification task. We attribute songs to DJs who would play the specific song. This new set of features and the focus on DJ sets is targeted at EDM as it takes the producer and DJ culture into account. With simple dimensionality reduction and machine learning these features enable us to attribute a song to a DJ with an accuracy of 63%. The features from the audio metering and monitoring tools in the recording studio could serve for many applications in Music Information Retrieval, such as genre, style and era classification and music recommendation for both DJs and consumers of electronic dance music.

We introduce Surfboard, an open-source Python library for extracting audio features with application to the medical domain. Surfboard is written with the aim of addressing pain points of existing libraries and facilitating joint use with modern machine learning frameworks. The package can be accessed both programmatically in Python and via its command line interface, allowing it to be easily integrated within machine learning workflows. It builds on state-of-the-art audio analysis packages and offers multiprocessing support for processing large workloads. We review similar frameworks and describe Surfboard's architecture, including the clinical motivation for its features. Using the mPower dataset, we illustrate Surfboard's application to a Parkinson's disease classification task, highlighting common pitfalls in existing research. The source code is opened up to the research community to facilitate future audio research in the clinical domain., Comment: 5 pages. 0 figures. Under review

We present a method for the analysis of the finger-string interaction in guitar performances and the computation of fine actions during the plucking gesture. The method is based on Motion Capture using high-speed cameras that can track the position of reflective markers placed on the guitar and fingers, in combination with audio analysis. A major problem inherent in optical motion capture is that of marker occlusion and, in guitar playing, it is the right hand of the guitarist that is extremely difficult to capture, especially during the plucking process, where the track of the markers at the fingertips is lost very frequently. This work presents two models that allow the reconstruction of the position of occluded markers: a rigid-body model to track the motion of the guitar strings and a flexible-body model to track the motion of the hands. In combination with audio analysis (onset and pitch detection), the method can estimate a comprehensive set of sound control features that include the plucked string, the plucking finger, and the characteristics of the plucking gesture in the phases of contact, pressure and release (e.g., position, timing, velocity, direction, or string displacement). This work has been sponsored by the European Union Horizon 2020 research and innovation program under grant agreement No. 688269 (TELMI project) and Beatriu de Pinos grant 2010 BP-A 00209 by the Catalan Research Agency (AGAUR).

Farm management practices done by machinery generate a high acoustical impact on animals. The acoustic variations in terms of equivalent level (Leq) and the different types of noise can affect the well-being of broilers by means of reducing the food and water ingest. In this work, we create a dataset in which we conduct a preliminary analysis of the acoustical impact generated by the farm management in an intensive broiler poultry farm of 25,000 birds. The project collects acoustic data during the first two weeks of the birds life, focusing the study on the first week. To create the dataset, we randomly select some files from each day of the study and they are analysed and labelled manually using an audio analysis software. The acoustical events defined in collaboration with the farmer and vet are the fan and the food and water supply, and definitions are based on duration, impact, and Signal to Noise Ratio (SNR). The analysis concludes that the main acoustical source in a broilers’ farm is the fan, and that it has a non-negligible acoustical impact. Nevertheless, the most frequent acoustical noise source active is food supply, but with less Leq impact.

The aim of this paper was to discover what combination of audio features gives the best performance with music emotion detection. Emotion recognition was treated as a regression problem, and a two-dimensional valence–arousal model was used to measure emotions in music. Features extracted by Essentia and Marsyas, tools for audio analysis and audio-based music information retrieval, were used. The influence of different feature sets was examined – low level, rhythm, tonal, and their combination – on arousal and valence prediction. The use of a combination of different types of features significantly improves the results compared with using just one group of features. Features particularly dedicated to the detection of arousal and valence separately, as well as features useful in both cases, were found and presented. This paper presents also the process of building emotion maps of musical compositions. The obtained emotion maps provide new knowledge about the distribution of emotions in an examined audio recording. They reveal new knowledge that had only been available to music experts until this point.

In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than +10% in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations. This research work is partially supported by Universidad de Alicante through the FPU program [UAFPU2014–5883] and the Spanish Ministerio de Economía y Competitividad through project TIMuL [No. TIN2013–48152–C2–1–R, supported by EU FEDER funds]. EB is supported by a UK RAEng Research Fellowship [grant number RF/128].

Estimating the pitch, or fundamental frequency, of a monophonic audio signal is a fundamental task in computational audio analysis with many downstream applications such as automatic transcription. The current general state of the art method for pitch detection is CRePE: a Convolutional Representation for Pitch Estimation. CRePE is a deep convolutional neural network designed to estimate the fundamental frequency of an audio signal directly from the waveform. However, CRePE, and other general pitch detection methods, do not perform well on steelpan audio. This is likely due to the steelpan's complex spectral characteristics that differentiate it timbrally from other sound sources. We combine a deep convolutional neural network architecture based on CRePE with a training dataset of steelpan audio from several distinct sounding tenor steelpans to achieve improved tenor steelpan pitch detection directly from the audio signal. We assess our model's ability to generalize by evaluating it with a test dataset that includes audio samples from steelpans that have no samples as part of the training set and compare these results to CRePE's performance on the same test dataset.

The estimation of fundamental frequency, or pitch, is a fundamental task in computational audio analysis with a variety of applications. Steelpan audio has proven difficult for general pitch detection methods CRePE and pYin. CRePE is a method that uses a deep convolutional neural network to perform pitch estimation directly from the audio signal while pYIN is a digital signal processing-based approach. Audio feature extraction is the process of using digital signal processing techniques to extract low level audio information from signals. Combining audio feature extraction with logistic regression is currently the best performing steelpan pitch estimation method, but the efficacy of using deep neural networks in lieu of traditional machine learning algorithms has yet to be determined. This paper compares the performance and computational requirements of a deep neural network-based architecture against logistic regression as well as the established pYIN and CRePE pitch detection methods to determine which method is the most accurate and efficient. All of these methods are evaluated on a test dataset containing one-hit audio samples from several distinct sounding steelpans. Generalization to other steelpans is assessed by including samples in the test dataset from steelpans for which no samples are in the training dataset.

Recent work at the Sussex Humanities Lab, a digital humanities research program at the University of Sussex, has sought to address an identified gap in the provision and use of audio feature analysis for spoken word collections. Traditionally, oral history methodologies and practices have placed emphasis on working with transcribed textual surrogates, rather than the digital audio files created during the interview process. This provides a pragmatic access to the basic semantic content, but obviates access to other potentially meaningful aural information; our work addresses the potential for methods to explore this extra-semantic information, by working with the audio directly. Audio analysis tools, such as those developed within the established field of Music Information Retrieval (MIR), provide this opportunity. This paper describes the application of audio analysis techniques and methods to spoken word collections. We demonstrate an approach using freely available audio and data analysis tools, which have been explored and evaluated in two workshops. We hope to inspire new forms of content analysis which complement semantic analysis with investigation into the more nuanced properties carried in audio signals.

Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri' or ‘Alexa'), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that specifically targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio filter banks to further lower computations. We find that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1× reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.

Background and objectives Diplophonia is a common symptom in voice disorders. Depending on the underlying aetiology, diplophonic patients typically need treatment such as phonosurgery or speech therapy. In current clinical practice, the presence of diplophonia is assessed by auditive rating. To avoid subjectivity in voice assessment and to follow principles of evidence based medicine, objective instrumental assessment methods are needed. In order to gain insight into instrumental assessment of diplophonic voice, comparisons between different assessment approaches are necessary. The aim of the study is to compare the performance of two independent objective approaches on their ability to detect diplophonia. The compared approaches are the formerly published degree of subharmonics (DSH), and a newly proposed measure for spatial bimodality of the vocal fold vibration. Material and methods From a clinical database of 352 laryngeal high-speed videos with synchronous audio recordings, 60 phonation segments (20 euphonic, twenty diplophonic and twenty non-diplophonic dysphonic) were auditively selected. For all phonation segments, the DSH and the newly proposed measure for spatial bimodality were determined. The DSH is the occurrence rate of audio analysis blocks with ambiguous fundamental frequency in percent. The bimodality measure quantifies the spatial occurrence of secondary oscillation frequencies along the vocal folds’ edges. Both the DSH and the bimodality measure are evaluated on their ability to detect diplophonia by means of cut off threshold classification. Results and conclusions The DSH showed excellent classification rates for separating diplophonic from euphonic phonation (sensitivity: 98.4%, specificity: 100%). In separating diplophonic from non-diplophonic dysphonic phonation, the bimodality measure slightly outperforms the DSH approach (sensitivity: 54.6%, specificity: 92.7%). The separation of diplophonia from other kinds of dysphonia is challenging, and more sophisticated methods are needed. It is concluded that auditive and glottal diplophonia must be distinguished. As the clinical assessment of diplophonia primarily aims at determining glottal conditions, the video-based approach might deliver clinically more relevant data than the auditive approach.

The article offers a method for determining vehicle speed from the audio track recorded by the dashboard camera mounted on the car in question. The author analyzed the operation of a four-stroke internal combustion engine, and established a dependency between the number of rotations (rpm) of the engine crankshaft and the number of rotations transferred to the wheel by the manual transmission. The author presents a formula that helps to determine the static radius of the wheel from its marking symbols on the tire. He looks at examples from forensic casework and offers options for determining the ID number of an engaged gearbox. Furthermore, he reviews the possibilities of applying this method to vehicles with an automatic transmission. In conclusion, the article presents the results of tests of the proposed method conducted by leading experts from various forensic institutions of the Russian Ministry of Justice. They point to the consistency of results when engine rpm is calculated based on cepstral analysis and engine tachometer readings.

This paper introduces the alpha version of a Python library called Iracema, which aims to provide models for the extraction of meaningful information from recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) list the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system.

The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.

This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation of the proposed algorithm yields overall results comparable to the baseline system. The main highlight is a reduction of the localization error on the evaluation dataset by a factor of 2.6, compared with the baseline performance., 5 pages, 5 figures, submitted to DCASE2019 Workshop

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28−31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25−28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc.

Graph theory is emerging as a new source of tools for time series analysis. One promising method is to transform a signal into its visibility graph, a representation which captures many interesting aspects of the signal. Here we introduce the visibility graph for audio spectra and propose a novel representation for audio analysis: the spectral visibility graph degree. Such representation inherently captures the harmonic content of the signal whilst being resilient to broadband noise. We present experiments demonstrating its utility to measure robust similarity between harmonic signals in real and synthesised audio data. The source code is available online., European Signal Processing Conference (EUSIPCO)