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Better than MFCC Audio Classification Features
https://research-repository.griffith.edu.au/bitstream/handle/10072/54461/81896_1.pdf?sequence=1
Better than MFCC Audio Classification Features Ruben Gonzalez Institute for Intelligent Integrated Systems, Griffith University, School of Information and Communication Technology, Gold Coast, Australia. Abstract. Mel-Frequency Ceptral Coeffienents (MFCCs) are generally the
Better Than MFCC Audio Classification Features
https://www.researchgate.net/publication/288034248_Better_Than_MFCC_Audio_Classification_Features
Download Citation | Better Than MFCC Audio Classification Features | Mel-Frequency Ceptral Coeffienents (MFCCs) are generally the features of choice for both audio classification and content-based ...
Better Than MFCC Audio Classification Features | SpringerLink
https://link.springer.com/chapter/10.1007%2F978-1-4614-3501-3_24
Mel-Frequency Ceptral Coeffienents (MFCCs) are generally the features of choice for both audio classification and content-based retrieval due to their proven performance. This paper presents alternate feature sets that not only consistently outperform MFCC features but are simpler to calculate.
Better Than MFCC Audio Classification Features - CORE …
https://core.ac.uk/reader/143870996
Better Than MFCC Audio Classification Features - CORE Reader
Features for audio classification
http://www.jeroenbreebaart.com/papers/soia/soia2004.pdf
FEATURES FOR AUDIO CLASSIFICATION ... discrimination, that the 2nd-order statistics of features (over time) are better features for classifi-cation than the features themselves [4]. Here we carry the temporal analysis one step further and ... 2.1.2 MFCC The second feature set is based on the first 13 MFCCs [24]. The final feature vector ...
Python audio signal classification MFCC features neural ...
https://stackoverflow.com/questions/32304432/python-audio-signal-classification-mfcc-features-neural-network
For this purpose I am extracting MFCC features of the audio signal and feed them into a simple neural network (FeedForwardNetwork trained with BackpropTrainer from PyBrain). Unfortunately the results are very bad. From the 5 classes the network seems to almost always come up with the same class as a result.
How I Understood: What features to consider while …
https://towardsdatascience.com/how-i-understood-what-features-to-consider-while-training-audio-files-eedfb6e9002b
Extraction of some of the features using Python has also been put up below. Some of the main audio features: (1) MFCC (Mel-Frequency Cepstral Coefficients): A.k.a ‘Most-frequently cons i dered coefficients’, MFCC is that one feature you would see being used in any machine learning experiment involving audio files.
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