Guqin is an ancient Chinese zither instrument known for its timbral variability and the vital role timbre, as opposed to melody or rhythm, played in its classical compositions. Numerous ancient texts dating back to the 1500s provided gestural guidelines of defined Guqin playing techniques and recommendations on timbre aesthetics. It’s also suggested in these texts that small deviations in gestures have significant impact on resulting timbres. Nevertheless, traditionally and even today, Guqin pedagogies are largely metaphoric, mind instead of body, and include limited elaboration on recommended gestures. To digitize and concretize the sonic implications in Guqin gesture-timbre writings, and variegate within the oversimplified vocabulary of playing techniques, this study aims to design and record a dataset of isolated, short, representative Guqin sounds labeled by gestural data. The sounds in question are curated by extracting ancient text, where emphasis on gesture-induced timbral difference is mentioned. We decompose the notion of gesture into nine degrees of freedom for both hands, including left/right hand position, fingers used, point of contact, left/right hand temporal coordination, etc. We define a ladder of gestural data at various levels, ranging from discrete labels of playing techniques, the aforementioned degrees of freedom to continuous signals acquired by high-speed camera with automatic hand-tracking system. We analyze in time-frequency domain timbres resulting from conventional playing gestures and their systematically “perturbed” versions. We investigate the correlation between timbres and their underlying gestures, via methods derived from multidimensional scaling.
Tag: music
Le guqin : vers une cartographie gestuelle et timbrale
Le 1er décembre 2023 à 14h45, Han Han présentera son projet sur la cartographie gestuelle et timbrale du guqin (cithare chinoise) à la bibliothèque La Grange Fleuret, au 11 bis rue de Vézelay à Paris.
Ce projet s’inscrit dans les “collaborations entre jeunes chercheuses et artistes” et est soutenu par l’association française d’informatique musicale (AFIM) et la société française d’ethnomusicologie (SFE).
Kymatio notebooks @ ISMIR 2023
On November 5th, 2023, we hosted a tutorial on Kymatio, entitled “Deep Learning meets Wavelet Theory for Music Signal Processing”, as part of the International Society for Music Information Retrieval (ISMIR) conference in Milan, Italy.
The Jupyter notebooks below were authored by Chris Mitcheltree and Cyrus Vahidi from Queen Mary University of London.
16 novembre 2023 : journée GdR ISIS “Traitement du signal pour la musique”
Dans le cadre de l’action « Traitement du signal pour l’audio et l’écoute artificielle » du GdR ISIS, nous organisons, le Jeudi 16 Novembre 2023 à l’IRCAM, une troisième journée dédiée au traitement des signaux de musique
Perceptual musical similarity metric learning with graph neural networks @ IEEE WASPAA
Sound retrieval for assisted music composition depends on evaluating similarity between musical instrument sounds, which is partly influenced by playing techniques. Previous methods utilizing Euclidean nearest neighbours over acoustic features show some limitations in retrieving sounds sharing equivalent timbral properties, but potentially generated using a different instrument, playing technique, pitch or dynamic. In this paper,… Continue reading Perceptual musical similarity metric learning with graph neural networks @ IEEE WASPAA
Zero-Note Samba: Self-supervised beat tracking @ IEEE TASLP
Supervised machine learning for music information retrieval requires a large annotated training set, and thus a high cognitive workload. To circumvent this problem, we propose to train deep neural networks to perceive beats in musical recordings despite having little or no access to human annotations. The key idea, which we name “Zero-Note Samba” (ZeroNS), is to train two fully convolutional networks in parallel: the first analyzes the percussive part of a musical piece whilst the second analyzes its non-percussive part. These networks learn a self-supervised pretext task of synchrony prediction (sync-pred), which simulates the ability of musicians to groove together when playing in the same band. Sync-pred encourages the two networks to return similar outputs if the underlying musical parts are synchronized, yet dissimilar outputs if the parts are out of sync. In practice, we obtain the instrumental parts from commercial recordings via an off-the-shelf source separation system: Spleeter. After self-supervised learning with sync-pred, ZeroNS produces a sparse output that resembles a beat detection function. When used in conjunction with a dynamic Bayesian network, ZeroNS surpasses the state of the art in unsupervised beat tracking. Furthermore, fine-tuning ZeroNS to a small set of labeled data (of the order of one to ten songs) matches the performance of a fully supervised network on 96 songs. Lastly, we show that pre-training a supervised model with sync-pred mitigates dataset bias and thus improves cross-dataset generalization, at no extra annotation cost.
Explainable audio classification of playing techniques with layerwise relevance propagation @ IEEE ICASSP
Deep convolutional networks (convnets) in the time-frequency domain can learn an accurate and fine-grained categorization of sounds. For example, in the context of music signal analysis, this categorization may correspond to a taxonomy of playing techniques: vibrato, tremolo, trill, and so forth. However, convnets lack an explicit connection with the neurophysiological underpinnings of musical timbre perception. In this article, we propose a data-driven approach to explain audio classification in terms of physical attributes in sound production. We borrow from current literature in “explainable AI” (XAI) to study the predictions of a convnet which achieves an almost perfect score on a challenging task: i.e., the classification of five comparable real-world playing techniques from 30 instruments spanning seven octaves. Mapping the signal into the carrier-modulation domain using scattering transform, we decompose the networks’ predictions over this domain with layer-wise relevance propagation. We find that regions highly-relevant to the predictions localized around the physical attributes with which the playing techniques are performed.
L’innovation peut-elle conduire à plus de sobriété dans la musique enregistrée ?
Une table ronde sur les enjeux écologiques de la musique enregistrée, organisée par le Centre national de la musique (CNM) à l’occasion des Rencontres de l’innovation dans la musique 2023. Cette table ronde coïncide avec la sortie du recueil “Musique et données”. Avec : Modération : Emily Gonneau – Causa
Écologie de la musique numérique
Un article en langue française dans le dernier recueil du Centre national de la musique (CNM). En voici le résumé : Il est temps de renoncer à l’utopie d’une musique intégralement disponible, pour tout le monde, partout, tout de suite. Au contraire, le flux audio musical est matérialisé dans ses objets, limité dans ses architectures… Continue reading Écologie de la musique numérique
Announcing Kymatio tutorial @ ISMIR
We will present a tutorial on Kymatio at the International Society for Music Information Retrieval (ISMIR) Conference, held in Milan on November 5-9, 2023.
Kymatio: Deep Learning meets Wavelet Theory for Music Signal Processing