Bio Inspired Cochlear Cepstrogram

Bio-inspired cochlear cepstrograms (CCGRAMs) represent audio signals in a way mimicking the human auditory system's processing, offering robust features for various applications. Current research focuses on leveraging CCGRAMs within self-supervised learning frameworks, particularly contrastive learning, often employing masking techniques to enhance feature extraction and improve model performance in tasks like speech emotion recognition. This approach demonstrates improved robustness to noise and superior performance compared to traditional methods, with applications extending to anti-spoofing countermeasures in areas such as speaker verification. The resulting advancements contribute to more accurate and reliable audio analysis across diverse fields.