Predominant Musical Instrument Classification based on Spectral Features
This work addresses instrument classification in music information retrieval, but it is incremental as it applies existing methods to a known dataset without introducing new techniques.
The paper tackled the problem of predominant musical instrument classification using the IRMAS dataset, achieving a 79% accuracy with an SVM classifier, which outperformed other state-of-the-art models.
This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for this purpose. The data includes musical clips recorded from various sources in the last century, thus having a wide variety of audio quality. We have presented a very concise summary of past work in this domain. Having implemented various supervised learning algorithms for this classification task, SVM classifier has outperformed the other state-of-the-art models with an accuracy of 79%. We also implemented Unsupervised techniques out of which Hierarchical Clustering has performed well.