  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.
