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Vincent Lostanlen

QMUL School of Electronic Engineering and Computer Science

Centre for Digital Music Seminar Series

Lecture by Vincent Lostanlen, NYU

Date/time: 3pm-4pm, Monday, 17th September 2018

Location: GC (Graduate Centre) 2.22

Open to academics, students, alumni, public; all welcome. Admission is FREE, no pre-booking required.

Abstract: Convolutional operators in the time-frequency domain: what makes music special?

The past decade has witnessed a breakthrough of deep convolutional networks (convnets) in computer vision. Yet, in order to adapt these architectures to audio classification tasks, it rarely suffices to replace 2-d convolutions over the spatial coordinates of the image by 1-d convolutions over the temporal axis of the raw waveform. Instead, most convnets for automatic speech recognition (ASR), as well as detection and classification of acoustic scenes and events (DCASE), are trained on a 2-d representation of acoustic energy in the time-frequency, such as the pointwise logarithm of mel-frequency spectrogram (logmelspec). Such an association between logmelspec and convnet is progressively becoming the default baseline in bioacoustic classification, as once were MFCC-GMM-HMM pipelines. However, music information retrieval (MIR) has not benefited from the same steady progress: in long-lasting challenges such as automatic chord recognition or musical instrument classification, logmelspec-convnets only perform marginally better than shallower architectures, even in the large data regime. In this talk, I will argue that a major obstacle to the applicability of convnets in MIR, above the lack of large-scale annotated datasets, is the inadequacy of 2-d spectrotemporal receptive fields for modeling some of the essential attributes of music perception: pitch, harmony, tempo, and meter. In particular, octave equivalence -- that is, the cognitive wrapping of a continuous frequency axis onto a spiral which makes a full turn at every octave -- is too often addressed merely as a spurious factor of intra-class in music signal analysis, rather than a resource for improving sparsity and reducing statistical overfitting in learned representations. After reviewing some of the most compelling arguments in favor of octave equivalence (from music theory, ethnomusicology, auditory neuroscience, and unsupervised learning), I will address the question of building convolutional operators in the time-frequency domain that disentangle temporal variations in pitch chroma from variations in pitch height. spiral scattering transform (Lostanlen, DAF-x 2015) and spiral convolutional networks (Lostanlen, DAF-x 2016). While the former enjoys a better theoretical interpretability, in terms of linearization of velocity parameters in the harmonic source-filter model, the latter can be readily integrated into an end-to-end learning pipeline for multipitch estimation in polyphonic music signals. Then, I will discuss future research directions, at the intersection of graph signal processing and the neo-Riemannian music theory: diagonalizing the Laplacian operator of the Tonnetz graph yields a basis of so-called "eigenprogressions" (Lostanlen, ISMIR-LBD 2018), i.e. Fourier-like atoms in the chord space of major and minor triads that are informative of harmonic similarity while being invariant to musical key, thus leading to state-of-the-art results in a task of supervised composer recognition from symbolic music data.cot2017/). His most recent projects involve the design and evaluation of systems to support 1) therapeutic gait training using Rhythmic Auditory Stimulation (RAS), 2) auditory training and second language learning.

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