Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds

TL;DR

This paper adapts speaker recognition techniques to learn and evaluate embedding spaces for musical instrument sounds, enabling recognition of unseen instruments for music synthesis applications.

Contribution

It introduces a novel approach using ASV architectures and evaluation methods for musical instrument sound embeddings, demonstrating effectiveness on multiple datasets.

Findings

Effective recognition of unseen instruments via EER metrics

Data augmentation and angular softmax improve embedding quality

Multi-task learning with instrument family labels enhances embedding structure

Abstract

Constructing an embedding space for musical instrument sounds that can meaningfully represent new and unseen instruments is important for downstream music generation tasks such as multi-instrument synthesis and timbre transfer. The framework of Automatic Speaker Verification (ASV) provides us with architectures and evaluation methodologies for verifying the identities of unseen speakers, and these can be repurposed for the task of learning and evaluating a musical instrument sound embedding space that can support unseen instruments. Borrowing from state-of-the-art ASV techniques, we construct a musical instrument recognition model that uses a SincNet front-end, a ResNet architecture, and an angular softmax objective function. Experiments on the NSynth and RWC datasets show our model's effectiveness in terms of equal error rate (EER) for unseen instruments, and ablation studies show the importance of data augmentation and the angular softmax objective. Experiments also show the benefit of using a CQT-based filterbank for initializing SincNet over a Mel filterbank initialization. Further complementary analysis of the learned embedding space is conducted with t-SNE visualizations and probing classification tasks, which show that including instrument family labels as a multi-task learning target can help to regularize the embedding space and incorporate useful structure, and that meaningful information such as playing style, which was not included during training, is contained in the embeddings of unseen instruments.