Content-Based Music Information Retrieval: Current Directions and Future Challenges

By Michael A. Casey, Remco C. Veltkamp, Masataka Goto, Marc Leman, Christophe Rhodes, Malcolm Slaney. Proceedings of the IEEE, 2008

718 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e2b0bd0ca5bca2a828faf19715aa1cf437773922

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, background): “Casey et al, 2008 ) and in educational contexts (e.”
A Survey of Audio-Based Music Classification and Annotation (2011, background): “By the terminology of [69], low-level features have high specificity, which describe the characteristics of the audio”
Searching in one billion vectors: Re-rank with source coding (2011, methodology): “Approximate nearest neighbors (ANN) search methods [3, 10, 14, 15] are required to handle large databases, especially for c omputer vision [12] and music retrieval [2] applications.”
Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, background): “First, the high-level concepts cannot be described accurately due to the so-called semantic gap [5].”
Feature learning and deep architectures: new directions for music informatics (2013, background): “…review the state of the art in these music signal processing systems across a variety of different application areas (Klapuri and Davy 2006; Casey et al. 2008; Müller et al. 2011), including melody extraction, chord recognition, beat tracking, tempo estimation, instrument identification,…”
Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics (2012, background): “[8, 22, 29]) amply document what is a decades-long research effort at the intersection of music, machine learning and signal processing, with wide applicability to a range of tasks including the automatic identification of melodies, chords, instrumentation, tempo, long-term structure, genre, artist,”
Automatic music transcription: challenges and future directions (2013, background): “where R << K, N; W ∈ RK×R + contains the spectral bases for each of the R pitch components; and H ∈ RR×N + is the pitch activity matrix across time. Applications of NMF for AMT include the work by Cont (2006), where sparseness constraints were added into the NMF update rules, in an effort to find me”
Parallel field alignment for cross media retrieval (2013, background): “To effectively leverage the massive explosion of multimedia content, a large number of approaches have been proposed in the areas such as information retrieval, multimedia retrieval and computer vision [2, 3, 5, 11, 16].”
Large Scale Nearest Neighbors Search Based on Neighborhood Graph (2013, background): “Finding the k-nearest neighbors (KNN) of a single query point in an extremely large collection of high-dimensional vectors is a fundamental problem in multimedia retrieval such as image retrieval [1, 2, 3], computer vision [4], music retrieval [5], etc.”