Investigating Multi-feature Selection And Ensembling For Audio Classification

Abstract

Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns--particularly in classification of audio data. For better performance of audio classification, feature selection and combination play a key role as they have the potential to make or break the performance of any DL model. To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i.e., Convolutional Neural Network, EfficientNet, MobileNet, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i.e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i.e., through ensembling) on three different datasets (i.e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset). Overall, results suggest feature sel

Stats

• citations37
• S2 citations—
• github stars0
• HF likes0
• heat score11.85
• arxiv keyturab2022investigating

Related papers

• Combining High-level Features Of Raw Audio Waves And Mel-spectrograms For Audio Tagging (2018)0.00
• Audio-based Music Classification With Densenet And Data Augmentation (2019)10.48
• An Empirical Study Of Visual Features For DNN Based Audio-visual Speech Enhancement In Multi-talker Environments (2020)3.58
• Spectral And Rhythm Features For Audio Classification With Deep Convolutional Neural Networks (2024)0.00
• A Comparative Re-assessment Of Feature Extractors For Deep Speaker Embeddings (2020)8.09
• Investigation Of Ensemble Features Of Self-supervised Pretrained Models For Automatic Speech Recognition (2022)9.41
• A Simple Fusion Of Deep And Shallow Learning For Acoustic Scene Classification (2018)0.00
• Audio Concept Classification With Hierarchical Deep Neural Networks (2017)0.00