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Obstacle avoidance is a major challenge during independent mobility for blind or visually impaired (BVI) people. Typically, BVI people can only perceive obstacles at a short distance (about 1 m, in case they are using the white cane), and some obstacles are hard to detect (e.g . , those elevated from the ground), or should not be hit by the white cane (e.g . , a standing person). A solution to these problems can be found in recent computer-vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them in real time. This contribution presents WatchOut , a sonification technique for conveying real-time information about the main properties of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centered approach, involving four iterations of online listening tests with BVI participants in order to define, improve and evaluate the sonification technique, eventually obtaining an almost perfect recognition accuracy. WatchOut was also implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable and can guide the users to avoid more than 85% of the obstacles.

This paper explores the behavior of an algorithm called Audio Dynamics Automatic Equalization (ADAE). This algorithm has been inspired by research carried out in the context of image restoration: it is the adaptation of a contrast and color unsupervised equalizer for images, called Automatic Color Equalization (ACE), into the audio domain. Beside testing if the domain shift from image to audio processing can bring some interesting result, this work also investigates if ADAE behaves like already-known technologies for audio manipulation and restoration. To this end, after a description of the original and the derived algorithms, quantitative test are carried out using typical analyses from the Sound and Music Computing literature, such as frequency response, transfer function, and harmonic distortion. Finally, the paper discusses how the algorithm introduces dynamic range adjustments and non-linear distortions, thus behaving like a dynamics processor, a harmonic exciter, and a waveshaper.

This paper presents PhonHarp, a new interface for musical expression. The instrument can be described as a human-device loop, based on an Android app for mobile phones that produces sounds, which are modified by the vocal tract of the musician. After analyzing the state of the art, this work will provide details about functional requirements, design choices, and implementation aspects.

Music shuffling is a common feature, available in most audio players and music streaming platforms. The goal of this function is to let songs be played in random, or constrained random, order. The results obtained by in-use shuffling algorithms can be unsatisfactory due to several factors including: the variability of user expectations to what constitutes a “successful” playlist, the common bias of being unable to recognize true randomness, and the tendency of humans to find nonexistent patterns in random structures. In this paper, a new shuffling algorithm called Ruffle is presented. Ruffle lets the user decide which aspects of the music library have to be actually shuffled, and which features should remain unchanged between consecutive extractions. First, an online survey was conducted to collect users’ feedback about the characteristics used for shuffling. It is worth noting that, in general, the algorithm could address any metadata and/or audio extracted feature. Then, in order to test the algorithm on personal playlists, a Web version based on Spotify API has been released. For this reason, a second survey is marking an ongoing effort placed on validating the effectiveness of the algorithm by collecting users’ feedback, and measuring the level of user satisfaction.

Multi-layer formats are becoming increasingly important in the field of music description. Thanks to their adoption, it is possible to embed into a unique digital document different representations of music contents, multiple in number and potentially heterogeneous in media type. Moreover, these descriptions can be mutually synchronized, thus providing different views of the same information entity with a customizable level of granularity. Standard use cases of multi-layer formats for music address information structuring and support to advanced fruition. The goal of the paper is to demonstrate how suitable multi-layer formats can foster analytical activities in the field of interpretative modelling and expressiveness investigation, discussing both the pedagogical roots and the educational implications of this approach. A use case focusing on the incipit of G. Mahler’s Symphony No. 5 will be presented.

This paper proposes a sonification for zCOSMOS, an astronomical dataset that contains information about 20,000 galaxies. The goals of such an initiative are multiple: providing a sound-based description of the dataset in order to make hidden features emerge, hybridizing science with art in a cross-domain framework, and treating scientific data as cultural heritage to be preserved and enhanced, thus breaking down the barriers between scientists and the general audience. In the paper, both technical and artistic aspects of the sonification will be addressed. Finally, some relevant excerpts from the resulting sonification will be presented and discussed.

Independent mobility is one of the main challenges for blind or visually impaired (BVI) people. In particular, BVI people often need to identify and avoid nearby obstacles, for example a bicycle parked on the sidewalk. This is generally achieved with a combination of residual vision, hearing and haptic sensing using the white cane. However, in many cases, BVI people can only perceive obstacles at short distance (typically about 1m, i.e., the white cane detection range), in other situations obstacles are hard to detect (e.g., those elevated from the ground), while others should not be hit by the white cane (e.g., a standing person). Thus, some time and effort are required to identify the object in order to understand how to avoid it. A solution to these problems can be found in recent computer vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them to a BVI user. This contribution presents WatchOut, a sonification technique for conveying real-time information about the main characteristics of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centric approach, involving two iterations of online questionnaires with BVI participants in order to define, improve and evaluate the sonification technique. WatchOut was implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable, and can guide the users to avoid more than 85% of the obstacles.

Simple Summary Cat vocalizations are their basic means of communication. They are particularly important in assessing their welfare status since they are indicative of information associated with the environment they were produced, the animal’s emotional state, etc. As such, this work proposes a fully automatic framework with the ability to process such vocalizations and reveal the context in which they were produced. To this end, we used suitable audio signal processing and pattern recognition algorithms. We recorded vocalizations from Maine Coon and European Shorthair breeds emitted in three different contexts, namely waiting for food, isolation in unfamiliar environment, and brushing. The obtained results are excellent, rendering the proposed framework particularly useful towards a better understanding of the acoustic communication between humans and cats. Abstract Cats employ vocalizations for communicating information, thus their sounds can carry a wide range of meanings. Concerning vocalization, an aspect of increasing relevance directly connected with the welfare of such animals is its emotional interpretation and the recognition of the production context. To this end, this work presents a proof of concept facilitating the automatic analysis of cat vocalizations based on signal processing and pattern recognition techniques, aimed at demonstrating if the emission context can be identified by meowing vocalizations, even if recorded in sub-optimal conditions. We rely on a dataset including vocalizations of Maine Coon and European Shorthair breeds emitted in three different contexts: waiting for food, isolation in unfamiliar environment, and brushing. Towards capturing the emission context, we extract two sets of acoustic parameters, i.e., mel-frequency cepstral coefficients and temporal modulation features. Subsequently, these are modeled using a classification scheme based on a directed acyclic graph dividing the problem space. The experiments we conducted demonstrate the superiority of such a scheme over a series of generative and discriminative classification solutions. These results open up new perspectives for deepening our knowledge of acoustic communication between humans and cats and, in general, between humans and animals.

Indoor navigation services for people with visual impairment are being researched in academia, and working systems have already been deployed in public places. While previous research mainly focuses on computing the user’s position with high accuracy, providing non-visual navigation instructions is also a challenge and naive approaches can fail in helping users reach their target destination or even expose them to hazards.In this paper we investigate the problem of guiding users to rotate towards a target direction. We propose three different sonification techniques that provide continuous guidance during rotation, and we compare them to a single-impulse baseline, used in previous works. We also explore three variations that reinforce the proposed techniques by combining them with the baseline. A preliminary study with 10 blind participants highlights two dominant techniques, which we analyze through a follow-up study with 18 participants, from 2 groups with very distant cultural backgrounds. While stark differences emerge in the performance from the two groups, we highlight two clear results common to both: 1) one of the proposed techniques significantly outperforms the baseline, reducing the average rotation error by a factor of 3.5 (from 11° to 3°); 2) the interaction speed of this technique, generally slower than the baseline, significantly improves when combined with the baseline technique.

Sonification is a fairly new term to scientists who are unaware of its multiple use cases. Even if some general definitions of the concept of sonification are commonly accepted, heterogeneous techniques - significantly different as it regards approaches, means and goals - are available. In this work we propose a reference system useful to interpret already-existing sonification instances and to plan new sonification tasks. This work aims to present a reference system for sonification using the inherent properties in the sonic output rather than the data itself. Validation has been conducted by automatically analyzing available experiments and examples, and placing them on the proposed sonification space, according to time-granularity and abstraction-level dimensions. This work can constitute the starting point for future research on computer-assisted sonification. It will be beneficial to a wide range of readers, in particular those from different disciplines looking at new ways to present and analyze data. HighlightsWe provide a classification of the most common sonification approaches.We propose a new diagram called the sonification space.We analyze a number of already available sonifications to validate the diagram.We address some open issues such as orthogonality in sound features.

Indoor navigation is an important service, currently investigated both in industry and academia. While the main focus of research is the computation of users' position, the additional challenge of conveying guidance instructions arises when the target user is blind or visually impaired (BVI). This contribution presents our ongoing research aimed at adopting sonification techniques to guide a BVI person. In particular we introduce three sonification techniques to guide the user during rotations. Preliminary results, conducted with 7 BVI people, show that some of the proposed sonification technique outperform a benchmark solution adopted in previous contributions.

Within a Music Information Retrieval perspective, the goal of the study presented here is to investigate the impact on sound features of the musician's affective intention, namely when trying to intentionally convey emotional contents via expressiveness. A preliminary experiment has been performed involving 10 tuba players. The recordings have been analysed by extracting a variety of features, which have been subsequently evaluated by combining both classic and machine learning statistical techniques. Results are reported and discussed.

Sound is an evanescent phenomenon that can convey information about the world around us. Unfortunately, explaining the production, propagation, and perception of sound to non-experts through a theoretical approach can be a goal hard to achieve. In this paper we will discuss an alternative way, based on an unmediated fruition of a multimedia installation originally conceived as an artwork. The idea is to use multiple sensory channels (i.e. hearing, touch and sight) in order to provide a low-level experience of sound-related phenomena through synesthetic experiential learning., Journal of e-Learning and Knowledge Society, Vol 13 No 1 (2017): Focus on: ICT and Experiential Learning: Models, Methodologies, Technologies, Research