钢琴调音软件的设计
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摘要
由于钢琴的日益普及,钢琴调音的需求也越来越大。一般调音都是专业调音师通过听力来判断钢琴的音准,受生理,心理以及客观环境的影响,难免会出现偏差。为此,本文设计并开发了一款用计算机实现钢琴调音的软件。
本文从钢琴音的物理特性和相应的物理学模型入手,分析论证了钢琴调音的基本方法。并从时域和频域两方面系统分析了乐音信号处理的方法,将语音识别技术中的一些算法应用于乐音识别,再结合钢琴调音的特点进行了改进。在基音检测方面,针对钢琴音域比语音宽,使用单一算法在整个频段内进行基音检测不能满足钢琴调音检测精度要求的问题,本文提出了分段式钢琴基音检测的方法:将钢琴的音域分为低频段和高频段,在低频段采用改进的自相关算法,在高频段采用谐波峰值法。实验证明这一方法对整个音域进行检测效果良好,达到钢琴调音检测精度要求。
本文所设计的钢琴调音软件实现了钢琴音的精确测量,完全可以满足实际钢琴调音的需要,并且也可以运用于其它弦乐器的调音工作。研究过程中所用到的理论和算法可用于后续其他声音信号识别的研究课题。
Because of the growing popularity of the piano, the demand of piano tuning is also growing. Generally professional monitors determine piano’s intonation through their hearing, and this must be influenced by physiology, psychology and objective environment .There will be some mistakes in it unavoidably. Therefore in this paper a software is designed to determine piano’s intonation by computer.
In this article we give the basic method of the piano tuning from the physical property of piano sound and the corresponding physics model. And from time and frequency range,we analysis musical note signal processing method systematically. And use some algorithms of speech signal processing in the musical note detection, then we make some advance according to the characteristics of the piano tuning. In the tone detection, because the piano sound range is wider than the speech range, using single algorithm can not satisfy the accuracy requirement, in this paper a method of measuring the basic frequency of the piano string by partitions is suggested to you. The range of piano tone is divided into two sections—the part of low frequency and another part of high frequency part. Improved autocorrelation is used in the part of low frequency and harmonics peak value method is used in another part. Experiments proved that this method is better than using a single algorithm to detect the pitch of piano.
The piano tuning software designed in this paper makes it possible for computers to measure the basic frequency of the piano string with high precision, and can fully meet the actual needs and can also utilize in other stringed musical instrument tuning work. The theories and methods used in the research can provide objective basis and ideas for the further work.
本文从钢琴音的物理特性和相应的物理学模型入手,分析论证了钢琴调音的基本方法。并从时域和频域两方面系统分析了乐音信号处理的方法,将语音识别技术中的一些算法应用于乐音识别,再结合钢琴调音的特点进行了改进。在基音检测方面,针对钢琴音域比语音宽,使用单一算法在整个频段内进行基音检测不能满足钢琴调音检测精度要求的问题,本文提出了分段式钢琴基音检测的方法:将钢琴的音域分为低频段和高频段,在低频段采用改进的自相关算法,在高频段采用谐波峰值法。实验证明这一方法对整个音域进行检测效果良好,达到钢琴调音检测精度要求。
本文所设计的钢琴调音软件实现了钢琴音的精确测量,完全可以满足实际钢琴调音的需要,并且也可以运用于其它弦乐器的调音工作。研究过程中所用到的理论和算法可用于后续其他声音信号识别的研究课题。
Because of the growing popularity of the piano, the demand of piano tuning is also growing. Generally professional monitors determine piano’s intonation through their hearing, and this must be influenced by physiology, psychology and objective environment .There will be some mistakes in it unavoidably. Therefore in this paper a software is designed to determine piano’s intonation by computer.
In this article we give the basic method of the piano tuning from the physical property of piano sound and the corresponding physics model. And from time and frequency range,we analysis musical note signal processing method systematically. And use some algorithms of speech signal processing in the musical note detection, then we make some advance according to the characteristics of the piano tuning. In the tone detection, because the piano sound range is wider than the speech range, using single algorithm can not satisfy the accuracy requirement, in this paper a method of measuring the basic frequency of the piano string by partitions is suggested to you. The range of piano tone is divided into two sections—the part of low frequency and another part of high frequency part. Improved autocorrelation is used in the part of low frequency and harmonics peak value method is used in another part. Experiments proved that this method is better than using a single algorithm to detect the pitch of piano.
The piano tuning software designed in this paper makes it possible for computers to measure the basic frequency of the piano string with high precision, and can fully meet the actual needs and can also utilize in other stringed musical instrument tuning work. The theories and methods used in the research can provide objective basis and ideas for the further work.
引文
[1]易克初,田斌,付强.语音信号处理[M].北京:国防工业出版社,2000. 3-5,24-25。
[2] Parson T W.语音信号处理{文成义等译}[M].北京:国防工业出版社,1990. 5-8。
[3]陈永彬,王仁华.语音信号处理[M].中国科学技术大学出版社,1990.3-6。
[4]张雄伟,陈亮等.现代语音信号处理技术及应用[M].机械工业出版社,2003. 7-10。
[5]刘丹,朱汉城.音乐特征识别的综述[J].计算机工程与应用.2002.74-77。
[6]管善群.音乐声学基础[M].上海:上海音乐出版社,1988.30-39。
[7]李重光.音乐理论基础[M].北京:人民音乐出版社,1962.1-4,40-66。
[8]李时中.谈钢琴弦列最佳弦点的理论与实践[J].乐器TS 953 35.14-20。
[9]姚亚平.音高结构历史演化概要[J].中国音乐学. 2000,1.99- 109。
[10]L.R.拉宾纳,R. W.谢弗编著.朱雪龙等译.语音信号数字处理[M].科学出版社,1987.78-90。
[11]赵立.语音信号处理[M].北京:机械工业出版社,2003.32-35.
[12]吴善培. Digital processing of speech signal[J].北京大学.研究生语音通信与处理教程.2003.123-137。
[13]王艳芬,于洪珍,刘卫东.基于时域自相关平方函数的基音周期估计[J].计算机工程与应用.2004,31.81-85。
[14] J P Haton et al. Automatic Speech Analysis and Recognition[M]. D Reidel Publishing Company,1982. 49-67。
[15]T Nagashima, J hawashima. Experimental study on arranging music by chaotic neural network[J].International Journal of Intelligent Systems.1997, Vol 12 (No 4) 323一339
[16]雷静.语音识别技术的研究及基本实现[D].武汉理工大学硕士学位论文. 2002.45-60。
[17]徐大为,吴边,赵建伟.一种噪声环境下的实时语音端点检测算法[J].计算机工程与应用.2003,39(1).115-117。
[18]Margaret L,Johnson.Toward an expert system for expressive musical performance[J].IEEE Computer.1991,24(7).30-34
[19]Kim Weaver,Khurram Waheed,Fathi M Salem.An entropy based robust speech boundary detection algorithm for realistic noisy environments[J].ICSP.2003.680-685。
[20]K Bozena. Computer-based recognition of musical phrase using the rough-set approach}J}.Intelligent Systems .1998, 104(1-2).15-30
[21]Kashino Kunio,Murasc Hiroshi.Music recognition using note transition context[J] ICASSP.1998,v5.93-96。
[22]何强,何英.MATLAB扩展编程[M].清华大学出版社,2002.185-230。
[23]周爱军,马海瑞.基于声卡的LabVIEW数据采集与分析系统设计[J].微计算机信息.2005,21(15).67-69。
[24]苏金明,黄国明,刘波. MATLAB与外部程序接口[M].北京:电子工业出,2004.50-68。
[25] M Marzinzik, B Kollmeier. Speech pause detection for noise spectrum estimation by tracking power envelope dynamics[J].ITSAP.2002, 10(2).109-117。
[26] Richard V.Cox, Candance A. Kamm. Speech and Language Processing for Next-Millennium Communication Services[J]. Proceedings of the IEEE. 2000,Vol. 88(8). 1314-1335。
[27]施小红,周佳.精通GUI图形界面编程[M].北京:北京大学出版社,2003. 69-90。
[28]Limaia Karray, Arnaud Martin. Towards improving speech detection robustness for speech recognition in adverse conditions[J]. Speech Communication, 2003, 40:261-276。
[29]J.P.Marques. de Sa著.吴逸飞译.Pattern Recognition Concepts Methods and Applications[M].清华大学出版社,2003.149-176。
[30]Masoud Alghomemy .Ahmed H Tewfik Rhythm and periodicity detection in polyphonic music IEEE signal Processing[J] .Society 1999 Workshop on Multimedia Signal Processing .1999,September 13-15.185-190。
[31]Moorer. An Approach for the Separation of Voices in Composite Musical Signals [D] USA Stanford University.1975.32-47。
[32]Hawley Structure out of Sound [D] USA MIT.1993.26-32。
[33]潘亚琼,李斌.基于PC机声卡的过程类复杂信号发生器[J].自动化仪表. 2005,26(9).14-18。
[2] Parson T W.语音信号处理{文成义等译}[M].北京:国防工业出版社,1990. 5-8。
[3]陈永彬,王仁华.语音信号处理[M].中国科学技术大学出版社,1990.3-6。
[4]张雄伟,陈亮等.现代语音信号处理技术及应用[M].机械工业出版社,2003. 7-10。
[5]刘丹,朱汉城.音乐特征识别的综述[J].计算机工程与应用.2002.74-77。
[6]管善群.音乐声学基础[M].上海:上海音乐出版社,1988.30-39。
[7]李重光.音乐理论基础[M].北京:人民音乐出版社,1962.1-4,40-66。
[8]李时中.谈钢琴弦列最佳弦点的理论与实践[J].乐器TS 953 35.14-20。
[9]姚亚平.音高结构历史演化概要[J].中国音乐学. 2000,1.99- 109。
[10]L.R.拉宾纳,R. W.谢弗编著.朱雪龙等译.语音信号数字处理[M].科学出版社,1987.78-90。
[11]赵立.语音信号处理[M].北京:机械工业出版社,2003.32-35.
[12]吴善培. Digital processing of speech signal[J].北京大学.研究生语音通信与处理教程.2003.123-137。
[13]王艳芬,于洪珍,刘卫东.基于时域自相关平方函数的基音周期估计[J].计算机工程与应用.2004,31.81-85。
[14] J P Haton et al. Automatic Speech Analysis and Recognition[M]. D Reidel Publishing Company,1982. 49-67。
[15]T Nagashima, J hawashima. Experimental study on arranging music by chaotic neural network[J].International Journal of Intelligent Systems.1997, Vol 12 (No 4) 323一339
[16]雷静.语音识别技术的研究及基本实现[D].武汉理工大学硕士学位论文. 2002.45-60。
[17]徐大为,吴边,赵建伟.一种噪声环境下的实时语音端点检测算法[J].计算机工程与应用.2003,39(1).115-117。
[18]Margaret L,Johnson.Toward an expert system for expressive musical performance[J].IEEE Computer.1991,24(7).30-34
[19]Kim Weaver,Khurram Waheed,Fathi M Salem.An entropy based robust speech boundary detection algorithm for realistic noisy environments[J].ICSP.2003.680-685。
[20]K Bozena. Computer-based recognition of musical phrase using the rough-set approach}J}.Intelligent Systems .1998, 104(1-2).15-30
[21]Kashino Kunio,Murasc Hiroshi.Music recognition using note transition context[J] ICASSP.1998,v5.93-96。
[22]何强,何英.MATLAB扩展编程[M].清华大学出版社,2002.185-230。
[23]周爱军,马海瑞.基于声卡的LabVIEW数据采集与分析系统设计[J].微计算机信息.2005,21(15).67-69。
[24]苏金明,黄国明,刘波. MATLAB与外部程序接口[M].北京:电子工业出,2004.50-68。
[25] M Marzinzik, B Kollmeier. Speech pause detection for noise spectrum estimation by tracking power envelope dynamics[J].ITSAP.2002, 10(2).109-117。
[26] Richard V.Cox, Candance A. Kamm. Speech and Language Processing for Next-Millennium Communication Services[J]. Proceedings of the IEEE. 2000,Vol. 88(8). 1314-1335。
[27]施小红,周佳.精通GUI图形界面编程[M].北京:北京大学出版社,2003. 69-90。
[28]Limaia Karray, Arnaud Martin. Towards improving speech detection robustness for speech recognition in adverse conditions[J]. Speech Communication, 2003, 40:261-276。
[29]J.P.Marques. de Sa著.吴逸飞译.Pattern Recognition Concepts Methods and Applications[M].清华大学出版社,2003.149-176。
[30]Masoud Alghomemy .Ahmed H Tewfik Rhythm and periodicity detection in polyphonic music IEEE signal Processing[J] .Society 1999 Workshop on Multimedia Signal Processing .1999,September 13-15.185-190。
[31]Moorer. An Approach for the Separation of Voices in Composite Musical Signals [D] USA Stanford University.1975.32-47。
[32]Hawley Structure out of Sound [D] USA MIT.1993.26-32。
[33]潘亚琼,李斌.基于PC机声卡的过程类复杂信号发生器[J].自动化仪表. 2005,26(9).14-18。