Classification of general audio data for content-based retrieval

By Dongge Li, I. Sethi, N. Dimitrova, T. McGee. Pattern Recognition Letters, 2001

313 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3568b09fb0b3395c94eba0c96f5ca3cd8d89584b

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Features for audio and music classification (2003, methodology): “This set of features is based on a recent paper by Li et al. (2001).”
Multimedia content processing through cross-modal association (2003, background): “A detailed discussion of our audio classification algorithm can be found in [18].”
Audio-based context recognition (2006, background): “Bandwidth is defined as the estimated bandwidth of the input signal [16].”
Diagnosing New York city's noises with ubiquitous data (2014, background): “Noise Understanding A number of works [2][5][9][11][13] have focused on classifying environmental noises so as to understand a user’s contexts, such as in a car or on a street.”
Multimodal Video Indexing: A Review of the State-of-the-art (2004, methodology): “A more elaborated audio segmentation algorithm is proposed in [43].”
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition (2019, background): “Early work on audio tagging included using manually-designed features as input, such as audio energy, zero-crossing rate, and mel-frequency cepstrum coefficients (MFCCs) [27].”
Music type classification by spectral contrast feature (2002, methodology): “Comparison with MFCC Mel-Frequency Cepstral Coefficients (MFCC) are widely used in audio classification [5] and music classification [3][4].”
Video summarization: methods and landscape (2003, methodology): “transcripts obtained from closed captions [1][7][16][22][23][32][41] or automatically generated using speech recognition [19].”
DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2012, methodology): “Energy Average energy of audio signal [59]”
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses (2015, methodology): “MFCC features (39) MFCC coefficients [20], Derivative of MFCC, MFCC Autocorrelation (AMFCC) Energy (1) and Pitch (1) Average energy of audio signal [20] and first pitch frequency Formants (4) Formants up to 4,400 Hz Time frequency (8) mean and std of: MSpectrum flux, Spectral centroid, Delta spectru”