Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Sound onset detection by applying psychoacoustic knowledge
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Analysis of the meter of acoustic musical signals
IEEE Transactions on Audio, Speech, and Language Processing
An experimental comparison of audio tempo induction algorithms
IEEE Transactions on Audio, Speech, and Language Processing
What/when causal expectation modelling applied to audio signals
Connection Science - Music, Brain, Cognition
Tempo and beat tracking for audio signals with music genre classification
International Journal of Intelligent Information and Database Systems
Three dimensions of pitched instrument onset detection
IEEE Transactions on Audio, Speech, and Language Processing
Correntropy function for fundamental frequency determination of musical instrument samples
Expert Systems with Applications: An International Journal
Musical onset detection by means of non-negative matrix factorization
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
A new method for musical onset detection in polyphonic piano music
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on amoving average.We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets.We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.