Detecting cover songs with pitch class key-invariant networks

Deep Learning (DL) has recently been applied successfully to the task of Cover Song Identification (CSI). Meanwhile, neural networks that consider music signal data structure in their design have been developed. In this paper, we propose a Pitch Class Key-Invariant Network, PiCKINet, for CSI. Like some other CSI networks, PiCKINet inputs a Constant-Q Transform (CQT) pitch feature. Unlike other such networks, large multi-octave kernels produce a latent representation with pitch class dimensions that are maintained throughout PiCKINet by key-invariant convolutions. PiCKINet is seen to be more effective, and efficient, than other CQT-based networks. We also propose an extended variant, PiCKINet+, that employs a centre loss penalty, squeeze and excite units, and octave swapping data augmentation. PiCKINet+ shows an improvement of ~17% MAP relative to the well-known CQTNet when tested on a set of ~16K tracks

CCOM-HuQin: an Annotated Multimodal Chinese Fiddle Performance Dataset

HuQin is a family of traditional Chinese bowed string instruments. Playing techniques(PTs) embodied in various playing styles add abundant emotional coloring and aesthetic feelings to HuQin performance. The complex applied techniques make HuQin music a challenging source for fundamental MIR tasks such as pitch analysis, transcription and score-audio alignment. In this paper, we present a multimodal performance dataset of HuQin music that contains audio-visual recordings of 11,992 single PT clips and 57 annotated musical pieces of classical excerpts. We systematically describe the HuQin PT taxonomy based on musicological theory and practical use cases. Then we introduce the dataset creation methodology and highlight the annotation principles featuring PTs. We analyze the statistics in different aspects to demonstrate the variety of PTs played in HuQin subcategories and perform preliminary experiments to show the potential applications of the dataset in various MIR tasks and cross-cultural music studies. Finally, we propose future work to be extended on the dataset.Comment: 15 pages, 11 figure

Adaptive Scattering Transforms for Playing Technique Recognition

Playing techniques contain distinctive information about musical expressivity and interpretation. Yet, current research in music signal analysis suffers from a scarcity of computational models for playing techniques, especially in the context of live performance. To address this problem, our paper develops a general framework for playing technique recognition. We propose the adaptive scattering transform, which refers to any scattering transform that includes a stage of data-driven dimensionality reduction over at least one of its wavelet variables, for representing playing techniques. Two adaptive scattering features are presented: frequency-adaptive scattering and direction-adaptive scattering. We analyse seven playing techniques: vibrato, tremolo, trill, flutter-tongue, acciaccatura, portamento, and glissando. To evaluate the proposed methodology, we create a new dataset containing full-length Chinese bamboo flute performances (CBFdataset) with expert playing technique annotations. Once trained on the proposed scattering representations, a support vector classifier achieves state-of-the-art results. We provide explanatory visualisations of scattering coefficients for each technique and verify the system over three additional datasets with various instrumental and vocal techniques: VPset, SOL, and VocalSet

Scattering Transform for Playing Technique Recognition

Playing techniques are expressive elements in music performances that carry important information about music expressivity and interpretation. When displaying playing techniques in the time–frequency domain, we observe that each has a distinctive spectro-temporal pattern. Based on the patterns of regularity, we group commonly-used playing techniques into two families: pitch modulation-based techniques (PMTs) and pitch evolution-based techniques (PETs). The former are periodic modulations that elaborate on stable pitches, including vibrato, tremolo, trill, and flutter-tongue; while the latter contain monotonic pitch changes, such as acciaccatura, portamento, and glissando. In this thesis, we present a general framework based on the scattering transform for playing technique recognition. We propose two variants of the scattering transform, the adaptive scattering and the direction-invariant joint scattering. The former provides highly-compact representations that are invariant to pitch transpositions for representing PMTs. The latter captures the spectro-temporal patterns exhibited by PETs. Using the proposed scattering representations as input, our recognition system achieves start-of-the-art results. We provide a formal interpretation of the role of each scattering component confirmed by explanatory visualisations. Whereas previously published datasets for playing technique analysis focused primarily on techniques recorded in isolation, we publicly release a new dataset to evaluate the proposed framework. The dataset, named CBFdataset, is the first dataset on the Chinese bamboo flute (CBF), containing full-length CBF performances and expert annotations of playing techniques. To provide evidence on the generalisability of the proposed framework, we test it over three additional datasets with a variety of playing techniques. Finally, to explore the applicability of the proposed scattering representations to general audio classification problems, we introduce two additional applications: one applies the adaptive scattering for identifying performers in polyphonic orchestral music and the other uses the joint scattering for detecting and classifying chick calls