On detection of the number of signals in presence of white noise
Journal of Multivariate Analysis
Fundamentals of speech recognition
Fundamentals of speech recognition
Multirate systems and filter banks
Multirate systems and filter banks
Musical understanding at the beat level: real-time beat tracking for audio signals
Computational auditory scene analysis
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evaluation System for Metrical Models
Computer Music Journal
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
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
Music tempo estimation with k-NN regression
IEEE Transactions on Audio, Speech, and Language Processing
An audio-driven virtual dance-teaching assistant
MM '11 Proceedings of the 19th ACM international conference on Multimedia
| Hi-index | 0.00 |
We present an innovative tempo estimation system that processes acoustic audio signals and does not use any high-level musical knowledge. Our proposal relies on a harmonic + noise decomposition of the audio signal by means of a subspace analysis method. Then, a technique to measure the degree of musical accentuation as a function of time is developed and separately applied to the harmonic and noise parts of the input signal. This is followed by a periodicity estimation block that calculates the salience of musical accents for a large number of potential periods. Next, a multipath dynamic programming searches among all the potential periodicities for the most consistent prospects through time, and finally the most energetic candidate is selected as tempo. Our proposal is validated using a manually annotated test-base containing 961 music signals from various musical genres. In addition, the performance of the algorithm under different configurations is compared. The robustness of the algorithm when processing signals of degraded quality is also measured.