A music similarity function based on signal analysis

By Beth T. Logan, Ariel Salomon. IEEE International Conference on Multimedia and Expo, 2001. ICME 2001., 2001

402 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/da3b939f4233e499eabfad7a84075b82b3312429

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In Signal Processing

Citations in the corpus

Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data (2008, methodology): “Hidden Markov models (HMMs) have been widely used to analyze sequential data, addressing problems in speech recognition [10], music analysis [8] and multiaspect target detection [11], among many others.”
Content-Based Music Information Retrieval: Current Directions and Future Challenges (2008, methodology): “Situated on the right-hand side of the specificity spectrum, an often-cited work is Logan and Salomon [ 19 ], who conducted experiments on audio similarity in a database of 8000 tracks, consisting of popular songs in different genres using histograms of 16 clusters of MFCC features per track with cl”
Universal Estimation of Information Measures for Analog Sources (2009, background): “Silva and Narayanan [221, 222] proposed an upper bound on the divergence for hidden Markov models and discussed its applications to speech recognition (see [21, 35, 128, 155, 175, 268] for more literature on this subject).”
A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “Song similarity is usually evaluated by comparing the distributions of short-term timbre features in each song [111]–[113].”
On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval (2014, background): “retrieval systems that support queries across content modalities, for example, using an image to search for texts.”
Aggregate features and ADABOOST for music classification (2006, neutral): “These include minimum distance and K-nearest neighbor in Lambrou et al. (1998), and Logan and Salomon (2001) .”
Music Similarity Measures: What's the use? (2002, methodology): “Finally, Logan in [9] has recently proposed a very similar approach to ours, which also uses Cepstrum Coefficients, only with a different modelling and a more complex matching algorithm.”
Multitask Bregman clustering (2010, background): “Actually, earth mover distance (EMD) [37] is a convenient choice for this problem, which is widely used in music analysis [38] and computer vision [37].”
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (2007, methodology): “Hidden Markov models (HMMs) have been widely used to analyze sequential data from a single source, addressing problems in speech recognition (Rabiner, 1989), music analysis (Logan & Salomon, 2001) and multiaspect target detection (Runkle et al., 1999).”
Music recommendation by unified hypergraph: combining social media information and music content (2010, methodology): “Acoustic measures of music similarity have been extensively studied in recent years [23, 37, 5, 25].”
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures (2004, methodology): “The techniques are further described in Logan and Salomon (2001) and Beren-zweig, Ellis, and Lawrence (2003) and are characterized by the features, models and distance measures used.”