Learning Audio And Image Representations With Bio-inspired Trainable Feature Extractors

Abstract

Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based on deep learning and convolutional neural networks, which require large amount of labeled training samples. In this work, we propose novel feature extractors that can be used to learn the representation of single prototype samples in an automatic configuration process. We employ the proposed feature extractors in applications of audio and image processing, and show their effectiveness on benchmark data sets.

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keystrisciuglio2018learning

Related papers

• Audioformer: Audio Transformer Learns Audio Feature Representations From Discrete Acoustic Codes (2023)0.00
• An Empirical Study Of Weakly Supervised Audio Tagging Embeddings For General Audio Representations (2022)0.00
• Audio-based Linguistic Feature Extraction For Enhancing Multi-lingual And Low-resource Text-to-speech (2024)0.00
• Audio Classification Of Low Feature Spectrograms Utilizing Convolutional Neural Networks (2024)5.84
• Deepvox: Discovering Features From Raw Audio For Speaker Recognition In Non-ideal Audio Signals (2020)0.00
• Embedded Emotions -- A Data Driven Approach To Learn Transferable Feature Representations From Raw Speech Input For Emotion Recognition (2020)0.00
• Audio ALBERT: A Lite BERT For Self-supervised Learning Of Audio Representation (2020)15.54
• Diverse Audio Embeddings -- Bringing Features Back Outperforms CLAP! (2023)0.00