A tutorial on onset detection in music signals

By Juan Pablo Bello, Laurent Daudet, S. Abdallah, C. Duxbury, M. Davies, Mark B. Sandler. IEEE Transactions on Speech and Audio Processing, 2005

908 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fe8d91583cf2638f9ff066c99ec75afb407da87f

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Citations in the corpus

Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks (2014, methodology): “Energy-based approaches [1, 7, 8] show that energy variations are quite reliable in discriminating onset position especially for hard onsets.”
Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching (2016, methodology): “While LMD-full cannot be used on its own to obtain ground truth for audio content-based MIR tasks, MIDI files themselves provide a useful mechanism of obtaining synthetic data, as demonstrated in chapter 4 and utilized for onset detection in (Bello et al., 2005).”
Phase Processing for Single-Channel Speech Enhancement: History and recent advances (2015, background): “Hence, the change of the IF from segment to segment—and its distribution—allow for the detection of transient sounds, such as note onsets [28].”
Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics (2012, background): “Much effort has been invested in determining which filters and functions make better onset detectors [3].”
Detecting harmonic change in musical audio (2006, methodology): “The three performance measures used here are Precision (P), the ratio of Hits to Detected Changes and Recall (R), the ratio of hits to transcribed changes and the f-measure (F) which combines the two (see equation 5) [ 1 ].”
Feature learning and deep architectures: new directions for music informatics (2013, background): “This led to in-depth studies of onset detection (Bello et al. 2005), based on the idea that “good” impulse-like signals, referred to as novelty functions, would greatly simplify periodicity analysis.”
Robust sound event detection in bioacoustic sensor networks (2019, methodology): “In spite of its simplicity, this deep learning model has shown to significantly outperform other algorithms for avian flight call detection in the BirdVox-full-night dataset, including spectral flux [87], the Vesper library reimplementation of the “Old Bird” energy-based detection function [65], and”
Automatic music transcription: challenges and future directions (2013, methodology): “Onset detection functions are typically computed from frequency-domain signals, using the band-wise magnitude and/or phase to compute spectral flux, phase deviation or complex domain detection functions (Bello et al. 2005; Dixon et al. 2006).”
Aggregate features and ADABOOST for music classification (2006, background): “A survey by Aucouturier and Pachet (2003) describes a number of popular features for music similarity and classification, and research continues (e.g. Bello et al. (2005), Pampalk et al. (2005)).”