Timbre representations of musical instruments, essential for diverse applications such as musical audio synthesis and separation, might be learned as bottleneck features from an instrumental recognition model. Given the similarities between speaker recognition and musical instrument recognition, in this paper, we investigate how to adapt successful speaker recognition algorithms to musical instrument recognition to learn meaningful instrumental timbre representations. To address the mismatch between musical audio and models devised for speech, we introduce a group of trainable filters to generate proper acoustic features from input raw waveforms, making it easier for a model to be optimized in an input-agnostic and end-to-end manner. Through experiments on both the NSynth and RWC databases in both musical instrument closed-set identification and open-set verification scenarios, the modified speaker recognition model was capable of generating discriminative embeddings for instrument and instrument-family identities. We further conducted extensive experiments to characterize the encoded information in learned timbre embeddings.
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