পদ্ধতির তুলনা করুন

	অডিও ফিঙ্গারপ্রিন্টিং	পিচ সনাক্তকরণ অ্যালগরিদম
ক্ষেত্র	সঙ্গীত তথ্য পুনরুদ্ধার	সঙ্গীত তথ্য পুনরুদ্ধার
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2002	2002
প্রবর্তক	Jeroen Haitsma	Alain de Cheveigné
ধরন	Perceptual audio hashing	Fundamental frequency estimation
মৌলিক উৎস	Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval.	de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.
অপর নাম	robust hashing, perceptual hashing, music identification	f0 detection, fundamental frequency tracking, monophonic pitch extraction
সম্পর্কিত	5	5
সারসংক্ষেপ	Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations.	Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem.