Sound morphing strategies based on alterations of time-frequency representations by Gabor multipliers

International audienceSounds morphing is an important topic in signal processing of musical sounds and covers a wide variety of techniques whose aim is to "interpolate" between two sound signals. We present here an approach based on the alteration of time-frequency representation. Time-frequency analysis is a classical tool in sounds analysis/synthesis. A time-frequency filter can be well-defined as a diagonal signal operator in a Gabor representation of sounds. Processing can be performed by multiplying a time-frequency representation with such a time-frequency filter, called a Gabor mask. After estimating such a Gabor mask between two sounds, we explore strategies to parametrize it for static morphing between two sounds. We then compare such an approach with standard and non standard approaches of morphing as different kind of sounds combination, notably classical means in the time-frequency domain

DDSP-Piano: A Neural Sound Synthesizer Informed by Instrument Knowledge

Instrument sound synthesis using deep neural networks has received numerous improvements over the last couple of years. Among them, the Differentiable Digital Signal Processing (DDSP) framework has modernized the spectral modeling paradigm by including signal-based synthesizers and effects into fully differentiable architectures. The present work extends the applications of DDSP to the task of polyphonic sound synthesis, with the proposal of a differentiable piano synthesizer conditioned on MIDI inputs. The model architecture is motivated by high-level acoustic modeling knowledge of the instrument, which, along with the sound structure priors inherent to the DDSP components, makes for a lightweight, interpretable, and realistic-sounding piano model. A subjective listening test has revealed that the proposed approach achieves better sound quality than a state-of-the-art neural-based piano synthesizer, but physical-modeling-based models still hold the best quality. Leveraging its interpretability and modularity, a qualitative analysis of the model behavior was also conducted: it highlights where additional modeling knowledge and optimization procedures could be inserted in order to improve the synthesis quality and the manipulation of sound properties. Eventually, the proposed differentiable synthesizer can be further used with other deep learning models for alternative musical tasks handling polyphonic audio and symbolic data

A temporally-constrained convolutive probabilistic model for pitch detection

A method for pitch detection which models the temporal evolution of musical sounds is presented in this paper. The proposed model is based on shift-invariant probabilistic latent component analysis, constrained by a hidden Markov model. The time-frequency representation of a produced musical note can be expressed by the model as a temporal sequence of spectral templates which can also be shifted over log-frequency. Thus, this approach can be effectively used for pitch detection in music signals that contain amplitude and frequency modulations. Experiments were performed using extracted sequences of spectral templates on monophonic music excerpts, where the proposed model outperforms a non-temporally constrained convolutive model for pitch detection. Finally, future directions are given for multipitch extensions of the proposed model

Music Information Retrieval in Live Coding: A Theoretical Framework

The work presented in this article has been partly conducted while the first author was at Georgia Tech from 2015–2017 with the support of the School of Music, the Center for Music Technology and Women in Music Tech at Georgia Tech. Another part of this research has been conducted while the first author was at Queen Mary University of London from 2017–2019 with the support of the AudioCommons project, funded by the European Commission through the Horizon 2020 programme, research and innovation grant 688382. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field

Frequency shifting approach towards textual transcription of heartbeat sounds

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription