Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A feature based on the group delay function from all-pole models (APGD) is proposed for environmental sound event recognition. The commonly used spectral features take into account merely the magnitude information, whereas the phase is overlooked due to the complications related to its interpretation. Additional information concealed in the phase is hypothesised to be beneficial for sound event recognition. The APGD is an approach to inferring phase information, which has shown applicability for speech and music analysis and is now studied in environmental audio. The evaluation is performed within a multi-label deep neural network (DNN) framework on a diverse real-life dataset of environmental sounds. It shows performance improvement compared to the baseline log mel-band energy case. Combined with the magnitude-based features, APGD demonstrates further improvement.
['Aleksandr Diment', 'Emre Cakir', 'Toni Heittola', 'Tuomas Virtanen']
I am writing a research paper on a idea Automatic recognition of environmental sound events using all-pole group delay features
[{"from": "human", "value": "I am writing a research paper on a idea Automatic recognition of environmental sound events using all-pole group delay features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A feature based on the group delay function from all-pole models (APGD) is proposed for environmental sound event recognition. The commonly used spectral features take into account merely the magnitude information, whereas the phase is overlooked due to the complications related to its interpretation. Additional information concealed in the phase is hypothesised to be beneficial for sound event recognition. The APGD is an approach to inferring phase information, which has shown applicability for speech and music analysis and is now studied in environmental audio. The evaluation is performed within a multi-label deep neural network (DNN) framework on a diverse real-life dataset of environmental sounds. It shows performance improvement compared to the baseline log mel-band energy case. Combined with the magnitude-based features, APGD demonstrates further improvement."}]