Comparison of Repeating Pattern Extraction Techniques for Audio Pitch Detection
1V. Sivaranjani, received her B.Tech Information technology Periyar Maniammai University, Thanjavur. Pursuing ME Computer and Science Engineering from Anna University, Chennai, Tamilnadu, India.
2J. Umamaheswari, received her BE Computer Science and Engineering Anna University Chennai, Tamilnadu. Pursuing ME Computer and Communication Engineering from Anna University, Chennai, Tamilnadu, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 02, 2014. | Manuscript published on May 05, 2014. | PP: 28-30 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2176054214/2014©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Music separation methods are more demanding and complex, demanding system “training,” user designation of special music features, and audio processing time to support their complicated frameworks. Pattern extraction from music strings is an complex problem. The repeated sequence extracted from music strings can be used as features for music extracted or compared. various works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. Present the REpeating Pattern Extraction Technique (REPET), a novel and simple approach for separating the repeating “background” from the non-repeating “foreground” in a mixture. The basic idea is to identify the periodically repeating segments in the audio, compare them to a repeating segment model derived from them, and extract the repeating patterns via time-frequency masking. But in proposed system doesn’t support the Small rhythmic patterns, but rhythmic patterns are essential for the balance of the music, and can be a way to identify a song. And enhanced a method to extract a monophonic rhythmic signature from a symbolic polyphonic score. To go beyond the simple extraction of all time intervals between onsets we select notes according to their length (short and long extractions) or their intensities (intensity+/− extractions). Once the frequency is calculated, now use dynamic programming to compare several sequences of audio.
Keywords: Pitch extraction, musical information retrieval, audio mining, pitch tracking, pattern extraction, audio segments.