| Paper: | MLSP-P3.12 | ||
| Session: | Speech and Audio Processing | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | CONTENT BASED AUDIO CLASSIFICATION AND RETRIEVAL USING JOINT TIME-FREQUENCY ANALYSIS | ||
| Authors: | Shahrzad Esmaili; Ryerson University | ||
| Sridhar Krishnan; Ryerson University | |||
| Kaamran Raahemifar; Ryerson University | |||
| Abstract: | In this paper, we present an audio classification and retrieval technique that exploits the non-stationary behavior of music signals and extracts features that characterize their spectral change over time. Audio classification provides a solution to incorrect and inefficient manual labelling of audio files on computers by allowing users to extract music files based on content similarity rather than labels. In our technique, classification is performed using time-frequency analysis and sounds are classified into 6 music groups consisting of rock, classical, folk, jazz and pop. For each 5-second music segment, the features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, and location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequency features are extracted and an accuracy of classification of around 93% using regular linear discriminant analysis or 92.3% using leave one out method is achieved. | ||
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