Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Acoustic scene classification is a difficult problem mostly due to the high density of events concurrently occurring in audio scenes. In order to capture the occurrences of these events  propose to use the Subband Por Distribution (SPD) as a feature.  extract it by computing the histogram of amplitude values in each frequency band of a spectrogram image. The SPD allows us to model the density of events in each frequency band. Our method is evaluated on a large acoustic scene dataset using support vector machines.  outperform the previous methods when using the SPD in conjunction with the histogram of gradients. To reach further improvement,  also consider the use of an approximation of the earth mover's distance kernel to compare histograms in a more suitable way. Using the so-called Sinkhorn kernel improves the results on most of the feature configurations. Best performances reach a 92.8% F1 score.
['Victor Bisot', 'Slim Essid', 'Gaël Richard']
I am writing a research paper on a idea HOG and subband power distribution image features for acoustic scene classification
[{"from": "human", "value": "I am writing a research paper on a idea HOG and subband power distribution image features for acoustic scene classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Acoustic scene classification is a difficult problem mostly due to the high density of events concurrently occurring in audio scenes. In order to capture the occurrences of these events  propose to use the Subband Por Distribution (SPD) as a feature.  extract it by computing the histogram of amplitude values in each frequency band of a spectrogram image. The SPD allows us to model the density of events in each frequency band. Our method is evaluated on a large acoustic scene dataset using support vector machines.  outperform the previous methods when using the SPD in conjunction with the histogram of gradients. To reach further improvement,  also consider the use of an approximation of the earth mover's distance kernel to compare histograms in a more suitable way. Using the so-called Sinkhorn kernel improves the results on most of the feature configurations. Best performances reach a 92.8% F1 score."}]