The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006 · 33 citations · 0 references
ComputingData ScienceAcousticsFeature EngineeringMachine LearningAuditory Scene AnalysisEnvironmental AcousticsSpeech RecognitionSupport Vector MachinesAcoustic Feature ExtractionAudio ClassificationAudio Feature ExtractionAudio Feature FusionClassifier FusionFeature Engineering For Audio SignalsSound Pattern RecognitionSupport Vector Machine Classification
In this paper, we present a new environmental sound classification architecture. The proposed sound classifier is performed in frame level and fuses the support vector machine (SVM) and the k nearest neighbor rule (KNN). In feature selection, three MPEG-7 audio low-level descriptors, spectrum centroid, spectrum spread, and spectrum flatness are used as the sound features to exploit their ability in sound classification. Experiments carried out on 12-class sound database can achieve an 85.1% accuracy rate. The The The performance comparison between the HMM sound classifier using audio spectrum projection features demonstrates the superiority of the proposed scheme.