Papers › Predominant Musical Instrument Classification based on Spectral Features
Predominant Musical Instrument Classification based on Spectral Features
Karthikeya Racharla, Vineet Kumar, Chaudhari Bhushan Jayant, Ankit Khairkar, Paturu Harish
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instrument Recognition | IRMAS | SVM | F1-score | 0.81 | #1 of 1 | Archive leaderboard | report |
| Instrument Recognition | IRMAS | SVM | Precision | 0.79 | #1 of 1 | Archive leaderboard | report |
| Instrument Recognition | IRMAS | SVM | Recall | 0.84 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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