Description: power spectrum analysis of the audio fingerprint system-power spectrum analysis of the audio finger rprint system Platform: |
Size: 1095 |
Author:雷柏英 |
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Description: use the matlab tool to do the mel analysis in the audio fingerprinting system.-use the Matlab tool to do the analysis in mel the audio fingerprinting system. Platform: |
Size: 2048 |
Author:雷柏英 |
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Description: power spectrum analysis of the audio fingerprint system-power spectrum analysis of the audio finger rprint system Platform: |
Size: 1024 |
Author:雷柏英 |
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Description: pHash is an implementation of various perceptual hashing algorithms. A perceptual hash is a fingerprint of an audio, video, or image file that is mathematically based on the audio or visual content contained within. Unlike cryptographic hash functions that rely on the avalanche effect of small changes in input leading to drastic changes in the output, perceptual hashes are "close" to one another if the inputs are visually or auditorily similar. As a result, perceptual hashes must also be robust enough to take into account transformations that could have been performed on the input. Platform: |
Size: 293888 |
Author:ssomsak |
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Description: 介绍了音频指纹技术的基本概念、性质和算法的基本框架，针对指纹在音频识别和音频认证中的应用，对现有的音频指纹算法进行了评价和比较。
-Introduces the basic concept of audio fingerprinting technology, nature and the algorithm for the basic framework, fingerprint authentication in audio recognition and audio the application of existing audio fingerprinting algorithm is evaluated and compared.
Platform: |
Size: 3559424 |
Author:孙丽娟 |
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Description: The basis for this comparison is a song’ s musical ‘ fingerprint’ , which is computed by
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics.-The basis for this comparison is a song’ s musical ‘ fingerprint’ , which is computed by
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics. Platform: |
Size: 222208 |
Author:ahmed |
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Description: Along with investigating similarity metrics between audio
material, the topic of robust matching of pairs of audio content
has gained wide interest recently. In particular, if this matching
process is carried out using a compact representation of the audio
content ("audio fingerprint"), it is possible to identify unknown
audio material by means of matching it to a database with the
fingerprints of registered works. This paper presents a system for
reliable, fast and robust identification of audio material which can
be run on the resources provided by today s standard computing
platforms. The system is based on a general pattern recognition
paradigm and exploits low level signal features standardized
within the MPEG-7 framework, thus enabling interoperability on
a world-wide scale. Platform: |
Size: 116736 |
Author:ahmed |
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Description: 1 Haar Wavelets
1.1 The Haar transform
1.2 Conservation and compaction of energy
1.3 Haar wavelets
1.4 Multiresolution analysis
1.5 Compression of audio signals
1.6 Removing noise from audio signals
1.7 Notes and references
2 Daub echies wavelets
2.1 The Daub4 wavelets
2.2 Conservation and compaction of energy
2.3 Other Daubechies wavelets
2.4 Compression of audio signals
2.5 Quantization, entropy, and compression
2.6 Denoising audio signals
2.7 Two-dimensional wavelet transforms
2.8 Compression of images
2.9 Fingerprint compression
2.10 Denoising images
2.11 Some topics in image processing
2.12 Notes and references
3 Frequency analysis
3.1 Discrete Fourier analysis
3.2 Definition of the DFT and its properties
3.3 Frequency description of wavelet analysis
3.4 Correlation and feature detection
3.5 Object detection in 2D images
3.6 Creating scaling signals and wavelets
3.7 Notes and references Platform: |
Size: 4108288 |
Author:Rakesh |
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Description: 数字音乐指纹提取的主耍目的是建立一种有效机制，用于比较2个音乐文件的听觉质量。提出一种基于小波包最优基分解的音
指纹提取算法，利用与音频内容密切相关的小波包系数，将其作为特征进行指纹提取。实验结果表明，该算法对MP3，WMA和RM压缩
噪声、Stirmark foraudio工具中常见的音频信号处理具有强鲁棒性，且在不同音乐之间具有较高可区分性。
关健词：数字音乐指纹；小波包变换；最优基；鲁棒性-The main playing digital music fingerprint extraction purpose is to establish an effective mechanism for the audio quality of the comparison of two music files. Proposed audio fingerprint extraction algorithm based on wavelet packet best basis decomposition, and audio content is closely related to the wavelet packet coefficients as the characteristic fingerprint extraction. The experimental results show that the algorithm compressed MP3, WMA and RM common noise, Stirmark foraudio Tools audio signal processing has strong robustness, and higher can distinguish between different music. Key words: digital music fingerprint wavelet packet transform optimal basis robustness Platform: |
Size: 295936 |
Author:寒雪 |
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Description: These routines implement a landmark-based audio fingerprinting system that is very well suited to identifying small, noisy excerpts a large number of items. It is based on the ideas used in the Shazam music matching service, which can identify seemingly any commercial music tracks short snippets recorded via cellphones even in very noisy conditions. I don t know if my algorithm is as good as theirs, but the approach, as described in the paper below, certainly seems to work:
Avery Wang An Industrial-Strength Audio Search Algorithm , Proc. 2003 ISMIR International Symposium on Music Information Retri , Baltimore, MD, Oct. 2003. http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf-These routines implement a landmark-based audio fingerprinting system that is very well suited to identifying small, noisy excerpts a large number of items. It is based on the ideas used in the Shazam music matching service, which can identify seemingly any commercial music tracks short snippets recorded via cellphones even in very noisy conditions. I don t know if my algorithm is as good as theirs, but the approach, as described in the paper below, certainly seems to work:
Avery Wang An Industrial-Strength Audio Search Algorithm , Proc. 2003 ISMIR International Symposium on Music Information Retri , Baltimore, MD, Oct. 2003. http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf Platform: |
Size: 198656 |
Author:desinfox@gmail.com |
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Description: 飞利浦音频指纹算法，是成功商业应用的先驱，成为学术界研究音频指纹的重要对象，经典，可直接使用。-Philips audio fingerprinting algorithm, is a pioneer in the successful commercial application of academic research has become an important object of the audio fingerprint, classic, can be used directly. Platform: |
Size: 1024 |
Author:睿子 |
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Description: 一种通用的音频指纹算法，代码很全面，可以使用，使用时请多看看里面的说明(A general audio fingerprint algorithm, the code is very comprehensive) Platform: |
Size: 202752 |
Author:刨个坑种西瓜
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Search - audio fingerprint