Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present a system to automatically learn features from audio in an unsupervised manner. Our method first learns an overcomplete dictionary which can be used to sparsely decompose log-scaled spectrograms. It then trains an efficient encoder which quickly maps new inputs to approximations of their sparse representations using the learned dictionary. This avoids expensive iterative procedures usually required to infer sparse codes.  then use these sparse codes as inputs for a linear Support Vector Machine (SVM). Our system achieves 83.4% accuracy in predicting genres on the GTZAN dataset, which is competitive with current state-of-the-art approaches. Furthermore, the use of a simple linear classifier combined with a fast feature extraction system allows our approach to scale ll to large datasets.
['Mikael Henaff', 'Kevin Jarrett', 'Koray Kavukcuoglu', 'Yann LeCun']
I am writing a research paper on a idea UNSUPERVISED LEARNING OF SPARSE FEATURES FOR SCALABLE AUDIO CLASSIFICATION
[{"from": "human", "value": "I am writing a research paper on a idea UNSUPERVISED LEARNING OF SPARSE FEATURES FOR SCALABLE AUDIO CLASSIFICATION"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present a system to automatically learn features from audio in an unsupervised manner. Our method first learns an overcomplete dictionary which can be used to sparsely decompose log-scaled spectrograms. It then trains an efficient encoder which quickly maps new inputs to approximations of their sparse representations using the learned dictionary. This avoids expensive iterative procedures usually required to infer sparse codes.  then use these sparse codes as inputs for a linear Support Vector Machine (SVM). Our system achieves 83.4% accuracy in predicting genres on the GTZAN dataset, which is competitive with current state-of-the-art approaches. Furthermore, the use of a simple linear classifier combined with a fast feature extraction system allows our approach to scale ll to large datasets."}]