This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are shown to be mostly orthogonal between these distance spaces. These results are considered with a focus recommendation and discovery applications employing these hybrid measures as their basis.

The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.

We propose an automatic method for measuring content-based music similarity, enhancing the current generation of music search engines and recommended systems. Many previous approaches to track similarity require brute-force, pair-wise processing between all audio features in a database and therefore are not practical for large collections. However, in an Internet-connected world, where users have access to millions of musical tracks, efficiency is crucial. Our approach uses features extracted from unlabeled audio data and near-neigbor retrieval using a distance threshold, determined by analysis, to solve a range of retrieval tasks. The tasks require temporal features-analogous to the technique of shingling used for text retrieval. To measure similarity, we count pairs of audio shingles, between a query and target track, that are below a distance threshold. The distribution of between-shingle distances is different for each database; therefore, we present an analysis of the distribution of minimum distances between shingles and a method for estimating a distance threshold for optimal retrieval performance. The method is compatible with locality-sensitive hashing (LSH)-allowing implementation with retrieval times several orders of magnitude faster than those using exhaustive distance computations. We evaluate the performance of our proposed method on three contrasting music similarity tasks: retrieval of mis-attributed recordings (fingerprint), retrieval of the same work performed by different artists (cover songs), and retrieval of edited and sampled versions of a query track by remix artists (remixes). Our method achieves near-perfect performance in the first two tasks and 75% precision at 70% recall in the third task. Each task was performed on a test database comprising 4.5 million audio shingles.

We present an efficient algorithm for automatically detecting remixes of pop songs in large commercial collections. Remixes are closely related as commercial products but they are not closely related in their audio spectral content because of the nature of the remixing process. Therefore spectral modelling approaches to audio similarity fail to recognize them. We propose a new approach - that chops songs into small chunks called audio shingles - to recognize remixed songs. We model the distribution of pair-wise distances between shingles by two independent processes - one corresponding to remix content and the other corresponding to non-remix content in a database. A nearest neighbour algorithm groups songs if they share shingles drawn from the remix process. Our results show 1) log-chromagram shingles separate remixed from non-remixed content with 75%-75% precision-recall performance, cepstral coefficient features do not separate the two distributions adequately 2) increasing the observations from the remix distribution increases the separability. Efficient implementation follows from the separability of the distributions using locality sensitive hashing (LSH) which speeds up automatic grouping of remixes by between one to two orders of magnitude in a 2018-song test set.

This tutorial describes techniques essential for searching the large multimedia databases that are now common on the Internet. There are up to 10 million songs in commercial music catalogues and over 300 million images stored in online photo services such as Flickr. How can we find the music, videos or images we want? How can we organize such large collections: find duplicates, create links between similar documents, extract and annotate semantic structures from complex audiovisual documents? Conventional methods for handling large data sets, such as hashing, get us part of the way, but those methods may not straightforwardly be used for similarity-based matching and retrieval in audiovisual document collections. On the other hand, several elaborate methods from multimedia retrieval are available for semantic document analysis. Unfortunately, those methods generally do not scale for large data sets. Instead, new classes of algorithms combining the best of the two worlds of large data methods and semantic analysis are needed to handle large multimedia databases. Innovative methods such as locality sensitive hashing, which are based on randomized probes, are the new workhorses. This tutorial covers methods for multimedia retrieval on large document collections. Starting with audio retrieval, we describe both the theory (i.e., randomized algorithms for hashing) and the implementation details (how do you store hash values for millions of songs?). A special focus is on how to combine large data methods with semantically meaningful descriptors in order to facilitate efficient similarity-based retrieval. Besides audio, the tutorial also covers image, 3d motion and video retrieval.

This paper demonstrates the importance of temporal sequences for passage-level music information retrieval. A number of audio analysis problems are solved successfully by using models that throw away the temporal sequence data. This paper suggests that we do not have this luxury when we consider a more difficult problem: that is finding musically similar passages within a narrow range of musical styles or within a single musical piece. Our results demonstrate a significant improvement in performance for audio similarity measures using temporal sequences of features, and we show that quantizing the features to string-based representations also performs well, thus admitting efficient implementations based on string matching

A method for segmenting musical audio with a hierarchical timbre model is introduced. New evidence is presented to show that music segmentation can be recast as clustering of timbre features, and a new clustering algorithm is described. A prototype thumbnail-generating application is described and evaluated. Experimental results are given, including comparison of machine and human segmentations

This paper describes a method for automatically segmenting and labelling sections in recordings of musical audio. We incorporate the user's expectations for segment duration as an explicit prior probability distribution in a Bayesian framework, and demonstrate experimentally that this method can produce accurate labelled segmentations for popular music

This research report describes an approach to parameter estimation for physical models of sound-generating systems using distal teachers and forward models (Jordan & Rumelhart, 1992; Jordan, 1990). The general problem is to find an inverse model of a sound-generating system that transforms sounds to action parameters; these parameters constitute a model-based description of the sound. We first show that a two-layer feedforward model is capable of performing inverse mappings for a simple physical model of a violin string. We refer to this learning strategy as direct inverse modeling; it requires an explicit teacher and it is only suitable for convex regions of the parameter space. A model of two strings was implemented that had non-convex regions in its parameter space. We show how the direct modeling strategy failed at the task of learning the inverse model in this case and that forward models can be used, in conjunction with distal teachers, to bias the learning of an inverse model so that non-convex regions are mapped to single-point solutions in the parameter space. Our results show that forward models are appropriate for learning to map sounds to parametric representations.