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Artificial reverberation (AR) models play a central role in various audio applications. Therefore, estimating the AR model parameters (ARPs) of a reference reverberation is a crucial task. Although a few recent deep-learning-based approaches have shown promising performance, their non-end-to-end training scheme prevents them from fully exploiting the potential of deep neural networks. This motivates the introduction of differentiable artificial reverberation (DAR) models, allowing loss gradients to be back-propagated end-to-end. However, implementing the AR models with their difference equations "as is" in the deep learning framework severely bottlenecks the training speed when executed with a parallel processor like GPU due to their infinite impulse response (IIR) components. We tackle this problem by replacing the IIR filters with finite impulse response (FIR) approximations with the frequency-sampling method. Using this technique, we implement three DAR models -- differentiable Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and Delay Network (DN). For each AR model, we train its ARP estimation networks for analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model counterpart. Experiment results show that the proposed method achieves consistent performance improvement over the non-end-to-end approaches in both objective metrics and subjective listening test results., Comment: Accepted to TASLP

In this paper, we present a novel method for harmonic-percussive sound separation (HPSS) by exploiting continuity/discontinuity properties in a matrix decomposition framework. It is widely accepted in the HPSS research that the harmonic and percussive components have anisotropic characteristics: The spectrum of the harmonic components and the time activation of the percussive components are sparse, whereas the spectrum of the percussive components and the time activation of the harmonic components are smooth. However, conventional methods fail to fully utilize the characteristics leading to suboptimal performance. Based on the observations that not the degree of sparseness but the degree of fluctuation is an accurate measure for distinguishing the harmonic and percussive components, we propose a novel HPSS algorithm by incorporating the continuity control in the iterative update formula of the matrix decomposition algorithm. In doing so, we first utilize probabilistic latent component analysis with Dirichlet prior, and later reformulate the algorithm in the nonnegative matrix factorization framework to reduce the computational cost. The comparative evaluation results show that the proposed method outperforms conventional methods in terms of both objective and subjective evaluation.

With the increasing amount of new motion-sensing hardware, improving the usability of gesture interfaces is becoming more important. However, people don't accept gesture interfaces as comfortably as traditional interfaces because they lack the tactile or haptic feedback of traditional input methods, such as mice or keyboards. However, having tactile feedback in gesture interfaces isn't possible unless users are wearing a device with actuators. Therefore, auditory feedback is an appropriate and unique alternative for assisting visual feedback in gesture interfaces. The authors propose various types of novel auditory feedback methods and explore their effects as secondary feedback on complementing visual feedback. They performed a user study for a menu selection task, and experimental results show that the proposed auditory feedback is significantly more efficient and effective than visual-only feedback or conventional auditory feedback in terms of time and accuracy.

In this letter, we describe a novel approach for separating a vocal signal from monaural music. We assume that the accompaniment in a music signal can be represented as the sum of the sustained harmonic and percussive sounds. Based on the observation that singing voices usually contain rapidly changing harmonic signals such as fast vibratos, slides, and/or glissandos, we propose a statistical model for the separation of harmonic/percussive and vocal sounds. To this end, we define an objective function that exploits the temporal/spectral continuities of harmonic/percussive sounds and the sparsity of vocal sounds in the spectrogram domain. Experimental results show that the proposed algorithm successfully separates the vocal from the accompaniment, resulting in a performance significantly better than that of conventional algorithms or comparable to the state-of-the-art algorithms.

Recommender systems have become an invaluable asset to online services with the ever-growing number of items and users. Most systems focused on recommendation accuracy, predicting likable items for each user. Such methods tend to generate popular and safe recommendations, but fail to introduce users to potentially risky, yet novel items that could help in increasing the variety of items consumed by the users. This is known as popularity bias, which is predominant in methods that adopt collaborative filtering. Recently, however, recommenders have started to improve their methods to generate lists that encompass diverse items that are both accurate and novel through specific novelty-driven algorithms or hybrid recommender systems. In this paper, we propose a recommender system that uses the concepts of Experts to find both novel and relevant recommendations. By analyzing the ratings of the users, the algorithm promotes special Experts from the user population to create novel recommendations for a target user. Thus, different users are promoted dynamically to Experts depending on who the recommendations are for. The system used data collected from Last.fm and was evaluated with several metrics. Results show that the proposed system outperforms matrix factorization methods in finding novel items and performs on par in finding simultaneously novel and relevant items. This system can also provide a means to popularity bias while preserving the advantages of collaborative filtering.

We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extremely laborious task of human annotation of chord names and boundaries-which must be done to provide machine learning models with ground truth-by performing automatic harmony analysis on symbolic music files. In parallel, we synthesize audio from the same symbolic files and extract acoustic feature vectors which are in perfect alignment with the labels. We, therefore, generate a large set of labeled training data with a minimal amount of human labor. This allows for richer models. Thus, we build 24 key-dependent HMMs, one for each key, using the key information derived from symbolic data. Each key model defines a unique state-transition characteristic and helps avoid confusions seen in the observation vector. Given acoustic input, we identify a musical key by choosing a key model with the maximum likelihood, and we obtain the chord sequence from the optimal state path of the corresponding key model, both of which are returned by a Viterbi decoder. This not only increases the chord recognition accuracy, but also gives key information. Experimental results show the models trained on synthesized data perform very well on real recordings, even though the labels automatically generated from symbolic data are not 100% accurate. We also demonstrate the robustness of the tonal centroid feature, which outperforms the conventional chroma feature.