{"title":"Musical instruments recognition using hidden Markov model","authors":"Jonghyun Lee, J. Chun","doi":"10.1109/ACSSC.2002.1197175","DOIUrl":null,"url":null,"abstract":"A new musical instrument recognition technique based on a hidden Markov model (HMM) is proposed. The spectral envelope is the key information of instrument characteristic and timbre. We decompose an instrument sound into sinusoidal components (harmonics) and noise components and estimate the amplitudes of the harmonics component. We want to express the spectral envelope effectively using estimated amplitude, therefore, we define three kinds of features and apply a recognition procedure to each feature. The HMM model used is continuous single Gaussian output HMM. To evaluate the performance of the recognition technique, the proposed technique is applied to classify the real instrumental sound of MUMS (MacGill University Master Samples). The recognition success ratio is more than 70%.","PeriodicalId":284950,"journal":{"name":"Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2002.1197175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
A new musical instrument recognition technique based on a hidden Markov model (HMM) is proposed. The spectral envelope is the key information of instrument characteristic and timbre. We decompose an instrument sound into sinusoidal components (harmonics) and noise components and estimate the amplitudes of the harmonics component. We want to express the spectral envelope effectively using estimated amplitude, therefore, we define three kinds of features and apply a recognition procedure to each feature. The HMM model used is continuous single Gaussian output HMM. To evaluate the performance of the recognition technique, the proposed technique is applied to classify the real instrumental sound of MUMS (MacGill University Master Samples). The recognition success ratio is more than 70%.
提出了一种新的基于隐马尔可夫模型的乐器识别技术。谱包络是表征仪器特性和音色的关键信息。我们将乐器声音分解为正弦分量(谐波)和噪声分量,并估计谐波分量的幅值。为了利用估计的幅度有效地表示谱包络,我们定义了三种特征,并对每种特征应用识别程序。所使用的HMM模型是连续的单高斯输出HMM。为了评估识别技术的性能,将所提出的技术应用于MUMS (MacGill University Master Samples)的真实器乐声音分类。识别成功率大于70%。