Name
Abstract | ||
|---|---|---|
This paper presents a novel feature extraction method for environmental sounds classification. Although many kinds of audio features have been investigated for environmental sound classification tasks, most of them have been extracted only to model the speech signal structure, which explains their lower performance when dealing with other kinds of audio signals. The method proposed in this paper processes and extracts the spectral changes throughout the frames of a sound file and appends them to the frame-based spectral feature vectors as dynamic features. Experimental results show that proposed features outperform the commonly used audio features in context recognition tasks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICICS.2011.6173513 | ICICS |
Keywords | Field | DocType |
audio signal processing,feature extraction,signal classification,spectral analysis,speech recognition,audio feature extraction method,audio signals,context recognition task,dynamic feature,environmental sound classification,frame-based spectral feature vector,sound file,spectral change extraction,spectral dynamic feature,speech signal extraction,audio classification,dynamic features,environmental sounds,vectors,filter bank,speech,feature vector,mel frequency cepstral coefficient,support vector machine,noise measurement | Environmental sounds,Audio signal,Mel-frequency cepstrum,Feature vector,Noise measurement,Pattern recognition,Computer science,Speech recognition,Feature extraction,Artificial intelligence,Audio signal processing,Environmental sound classification | Conference |
ISBN | Citations | PageRank |
978-1-4577-0029-3 | 6 | 0.55 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karbasi, M. | 1 | 6 | 0.55 |
| Ahadi, S.M. | 2 | 15 | 4.43 |
| Bahmanian, M. | 3 | 6 | 0.55 |