Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

doi:10.3837/tiis.2021.02.008

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…In addition, various feature extraction algorithms are used to detect the score and pitch information in the music. This study uses the zero-crossing rate, Mel-frequency cepstral coefficients, and chroma features in music detection [35].…”

Section: Music Detectionmentioning

confidence: 99%

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Park

Chung

Kim

2023

JWE

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Videos contain visual and auditory information. Visual information in a video can include images of people, objects, and the landscape, whereas auditory information includes voices, sound effects, background music, and the soundscape. The audio content can provide detailed information on the story by conducting a voice and atmosphere analysis of the sound effects and soundscape. Metadata tags represent the results of a media analysis as text. The tags can classify video content on social networking services, like YouTube. This paper presents the methodologies of speech, audio, and music processing. Also, we propose integrating these audio tagging methods and applying them in an audio metadata generation system for video storytelling. The proposed system automatically creates metadata tags based on speech, sound effects, and background music information from the audio input. The proposed system comprises five subsystems: (1) automatic speech recognition, which generates text from the linguistic sounds in the audio, (2) audio event classification for the type of sound effect, (3) audio scene classification for the type of place from the soundscape, (4) music detection for the background music, and (5) keyword extraction from the automatic speech recognition results. First, the audio signal is converted into a suitable form, which is subsequently combined from each subsystem to create metadata for the audio content. We evaluated the proposed system using video logs (vlogs) on YouTube. The proposed system exhibits a similar accuracy to handcrafted metadata for the audio content, and for a total of 104 YouTube vlogs, achieves an accuracy of 65.83%.

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Section: Music Detectionmentioning

confidence: 99%

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Park

Chung

Kim

2023

JWE

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Effect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Method

Köseoğlu

Uyanık

2023

Gazi University Journal of Science

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Audio search algorithms are used to detect the matching file in large databases, especially in multimedia applications and smart appliances. Based on different audio fingerprint extraction methods, similar algorithms have been developed and applied in different fields. These algorithms are expected to perform the detection in a reliable and robust way within the possible shortest time. In this study, based on spectral peaks method, an audio fingerprint algorithm with a few minor modifications was developed to accurately detect the matching audio file in the target database. The algorithm was demonstrated and then the effect of spectrogram parameters such as window size, overlap and number of FFT was investigated in terms of reliability and robustness of the program under three different noise sources. The database was a relatively small size one with five genres of music. In this study, it was aimed to contribute to new audio file detection studies based on spectral peaks method. It was observed that the the variation in the spectrogram parameters have significantly affected the number of matchings (NM), reliability and robustness. Under high noise conditions the optimal spectrogram parameters were determined as 512;50%;512, respectively. We did not observe, however, a significant effect of music genre on NM.

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Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

Cited by 2 publications

References 24 publications

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Effect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Method

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