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International audience; Music improvisation is the ability of musical generative systems to interact with either another music agent or a human improviser. This is a challenging task, as it is not trivial to define a quantitative measure that evaluates the creativity of the musical agent. It is also not feasible to create huge paired corpora of agents interacting with each other to train a critic system. In this paper we consider the problem of controlling machine improvisation by switching between several pre-trained models by finding the best match to an external control signal. We introduce a measure SymTE that searches for the best transfer entropy between representations of the generated and control signals over multiple generative models.

Choral music separation refers to the task of extracting tracks of voice parts (e.g., soprano, alto, tenor, and bass) from mixed audio. The lack of datasets has impeded research on this topic as previous work has only been able to train and evaluate models on a few minutes of choral music data due to copyright issues and dataset collection difficulties. In this paper, we investigate the use of synthesized training data for the source separation task on real choral music. We make three contributions: first, we provide an automated pipeline for synthesizing choral music data from sampled instrument plugins within controllable options for instrument expressiveness. This produces an 8.2-hour-long choral music dataset from the JSB Chorales Dataset and one can easily synthesize additional data. Second, we conduct an experiment to evaluate multiple separation models on available choral music separation datasets from previous work. To the best of our knowledge, this is the first experiment to comprehensively evaluate choral music separation. Third, experiments demonstrate that the synthesized choral data is of sufficient quality to improve the model's performance on real choral music datasets. This provides additional experimental statistics and data support for the choral music separation study., Camera Ready for Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR 2022

With the advancement in Deep Learning technologies, computers today are able to achieve unimaginable success in several domains involving images and audio. One such area in 3D audio where the applications of deep learning can be promising is in binaural sound localization for headphones, which requires individualized and accurate representations of the filtering effects of the anthropometric measurements of a listening body. Such filters often are stored as a set of Head Related Impulse Responses (HRIRs) or in their frequency domain representations, Head Related Transfer Functions (HRTFs), for specific individuals. A challenge in applying deep learning networks in this area is the lack of availability of vast numbers of complete and accurate HRTF datasets, which is known to cause networks to easily over-fit to the training data. As opposed to images, where the correlations between pixels are more statistical, the correlations that HRTFs share in space are expected to be more a function of the body and pinna reflections. We hypothesize that these spatial correlations between the elements of an HRTF set could be learned using Deep Convolutional Neural Networks (DCNNs). In this work, we first present a CNN-based auto-encoding strategy for HRTF encoding and then we use the learned auto-encoder to provide an alternate solution for the interpolation of HRTFs from a sparse distribution of HRTFs in space. We thereby conclude that DCNNs are capable of achieving results that are comparable to other non deep learning based approaches, in spite of using only a few tens of data points.