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Extended Playing Techniques: The Next Milestone in Musical Instrument Recognition

Workshop paper
Vincent Lostanlen, Joakim Andén, Mathieu Lagrange
Proceedings of the International Workshop on Digital Libraries for Musicology (DLfM)
Publication year: 2018

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single “ordinary” note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.

Transformée en scattering sur la spirale temps–chroma–octave

Workshop paper
Vincent Lostanlen, Stéphane Mallat
Actes du colloque GRETSI, 2015
Publication year: 2015

We introduce a scattering representation for the analysis and classification of sounds. It is locally translation-invariant, stable to deformations in time and frequency, and has the ability to capture harmonic structures. The scattering representation can be interpreted as a convolutional neural network which cascades a wavelet transform in time and along a harmonic spiral. We study its application for the analysis of the deformations of the source–filter model.

Joint Time–frequency Scattering for Audio Classification

Workshop paper
Joakim Andén, Vincent Lostanlen, and Stéphane Mallat
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Publication year: 2015

We introduce the joint time–frequency scattering transform, a time shift invariant descriptor of time–frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time–frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time–frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.