Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Signal noise reduction can improve the performance of machine learning systems dealing with time signals such as audio. Real-life applicability of these recognition technologies requires the system to uphold its performance level in variable, challenging conditions such as noisy environments. In this contribution,  investigate audio signal denoising methods in cepstral and log-spectral domains and compare them with common implementations of standard techniques. The different approaches are first compared generally using averaged acoustic distance metrics. They are then applied to automatic recognition of spontaneous and natural emotions under simulated smartphone-recorded noisy conditions. Emotion recognition is implemented as support vector regression for continuous-valued prediction of arousal and valence on a realistic multimodal database. In the experiments, the proposed methods are found to generally outperform standard noise reduction algorithms.
['Jouni Pohjalainen', 'Fabien Ringeval', 'Zixing Zhang', 'Björn W. Schuller']
I am writing a research paper on a idea Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Signal noise reduction can improve the performance of machine learning systems dealing with time signals such as audio. Real-life applicability of these recognition technologies requires the system to uphold its performance level in variable, challenging conditions such as noisy environments. In this contribution,  investigate audio signal denoising methods in cepstral and log-spectral domains and compare them with common implementations of standard techniques. The different approaches are first compared generally using averaged acoustic distance metrics. They are then applied to automatic recognition of spontaneous and natural emotions under simulated smartphone-recorded noisy conditions. Emotion recognition is implemented as support vector regression for continuous-valued prediction of arousal and valence on a realistic multimodal database. In the experiments, the proposed methods are found to generally outperform standard noise reduction algorithms."}]