Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Music Information Retrieval systems are commonly built on a feature extraction stage. For applications involving automatic classification (e.g. speech/music discrimination, music genre or mood recognition, ...), traditional approaches will consider a large set of audio features to be extracted on a large dataset. In some cases, this will lead to computationally intensive systems and there is, therefore, a strong need for efficient feature extraction. , a new audio feature extraction software, YAAFE 1 , is presented and compared to widely used libraries. The main advantage of YAAFE is a significantly lor complexity due to the appropriate exploitation of redundancy in the feature calculation. YAAFE remains easy to configure and each feature can be parameterized independently. Finally, the YAAFE framework and most of its core feature library are released in source code under the GNU Lesser General Public License.
['Benoît Mathieu', 'Slim Essid', 'Thomas Fillon', 'Jacques Prado', 'Gaël Richard']
I am writing a research paper on a idea YAAFE, an Easy to Use and Efficient Audio Feature Extraction Software.
[{"from": "human", "value": "I am writing a research paper on a idea YAAFE, an Easy to Use and Efficient Audio Feature Extraction Software."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Music Information Retrieval systems are commonly built on a feature extraction stage. For applications involving automatic classification (e.g. speech/music discrimination, music genre or mood recognition, ...), traditional approaches will consider a large set of audio features to be extracted on a large dataset. In some cases, this will lead to computationally intensive systems and there is, therefore, a strong need for efficient feature extraction. , a new audio feature extraction software, YAAFE 1 , is presented and compared to widely used libraries. The main advantage of YAAFE is a significantly lor complexity due to the appropriate exploitation of redundancy in the feature calculation. YAAFE remains easy to configure and each feature can be parameterized independently. Finally, the YAAFE framework and most of its core feature library are released in source code under the GNU Lesser General Public License."}]