| dc.contributor.author | Baskaran, Piruthuvi | |
| dc.date.accessioned | 2024-03-22T09:17:37Z | |
| dc.date.available | 2024-03-22T09:17:37Z | |
| dc.date.issued | 2023 | |
| dc.identifier.citation | Baskaran, Piruthuvi (2023) Song Pitch Detection and Giving Feedback and Corrections . BSc. Dissertation, Informatics Institute of Technology | en_US |
| dc.identifier.issn | 2019786 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/1938 | |
| dc.description.abstract | "Singly is an application that allows people who are planning to take part in live competitions, to insert their original song and the song which they have sung and get feedback according to the vocal pitch for every second. The main aim of this application is to train the competitors who are planning to take part in the live competition. By utilizing advanced algorithms such as Fast Fourier Transform (FFT) and Harmonic Peak Picking, Singly performs a comprehensive analysis of both audio files, comparing the pitch characteristics of the user's singing to the original song. The core algorithms employed in Singly are FFT and Harmonic Peak Picking. They form the foundation of its accurate pitch analysis. FFT transforms the audio signals into frequency domain representations, enabling the identification of pitch-related information. The Harmonic Peak Picking algorithm effectively extracts and analyzes the fundamental frequency and harmonics of the audio signals, aiding in the identification of pitch discrepancies. Singly serves as a valuable tool for users seeking to enhance their vocal performances. By highlighting specific areas where pitch errors occur and suggesting corrective measures. In conclusion, Singly presents a novel approach to pitch comparison and feedback in the realm of audio analysis. By leveraging FFT and Harmonic Peak Picking algorithms, the application provides valuable insights into users' pitch accuracy, enabling them to identify and address areas requiring improvement. Singly is tested and the performance testing and best practices testing scores were 100% but the accessibility testing score was only 42% which can be improved in the future. " | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | en_US | |
| dc.subject | Song Pitch | en_US |
| dc.subject | Harmonic peak picking Algorithm | en_US |
| dc.subject | Fast Fourier Transform (FFT) | en_US |
| dc.title | Song Pitch Detection and Giving Feedback and Corrections | en_US |
| dc.type | Thesis | en_US |