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Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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Musical note onset detection based on a spectral sparsity measure
Mina Mounir1, Peter Karsmakers2, Toon van Waterschoot1
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Kasteelpark Arenberg 10, Leuven, 3001 Belgium.
EURASIP Journal on Audio, Speech, and Music Processing
|November 1, 2021
Summary
This study introduces a new method for note onset detection (NOD) using a novel spectral sparsity feature called NINOS^2. This technique improves instrument identification and performance in complex musical pieces.
Area of Science:
- Audio Signal Processing
- Music Information Retrieval
Background:
- Note onset detection (NOD) is crucial for music analysis and information retrieval.
- Existing NOD methods face challenges with complex musical textures and specific instrument types.
Purpose of the Study:
- To propose a novel feature for improved note onset detection.
- To enhance the accuracy of identifying musical instrument characteristics from audio signals.
Main Methods:
- Development of the Normalized Identification of Note Onset based on Spectral Sparsity (NINOS^2) feature.
- Exploitation of spectral sparsity in low-magnitude spectral components for note onset identification.
- Extensive simulations across diverse instruments, playing styles, and mixed audio conditions.
Main Results:
- The NINOS^2 feature consistently outperforms the Logarithmic Spectral Flux (LSF) baseline for sustained-string instruments.
- Demonstrated superior performance in challenging scenarios like polyphonic music and vibrato performances.
- NINOS^2 effectively captures time-frequency characteristics for instrument identity.
Conclusions:
- The proposed NINOS^2 method offers a significant advancement in note onset detection.
- This feature enhances the analysis of musical structure and instrument timbre.
- NINOS^2 provides a robust solution for complex music information retrieval tasks.
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