Article citationsMore>>

Zhong, L., Cooper, E., Yamagishi, J., & Minematsu, N. (2023). Exploring Isolated Musical Notes as Pre-Training Data for Predominant Instrument Recognition in Polyphonic Music. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 2312-2319). IEEE.
https://doi.org/10.1109/apsipaasc58517.2023.10317292

has been cited by the following article:

• TITLE: Multi-Instrument Detection in Polyphonic Music with Cultural Instruments

  AUTHORS: Sovathanak Meas, Rezza Moieni

  KEYWORDS: Convolutional Neural Network, Multi-Instrument Detection, Cultural Instruments, Deep Learning, Multi-Label Classification

  JOURNAL NAME: Open Journal of Social Sciences, Vol.13 No.9, September 22, 2025

  ABSTRACT: The study adapts several machine-learning and deep-learning architectures to recognize 63 traditional instruments in weakly labelled, polyphonic audio synthesized from the proprietary Sound Infusion collection. Ten thousand 5s clips were algorithmically generated, features such as Mel-spectrograms, MFCCs, and VGGish embeddings were extracted, and six models were evaluated. The re-implemented Han et al. Convolutional Neural Network (CNN) attained the best result (micro F1 = 0.55; macro F1 = 0.50), approaching published performance on mainstream Instrument Recognition in Musical Audio Signals (IRMAS) data. Results highlight data scarcity and class imbalance as key obstacles for culturally diverse MIR.