Content-based Transformations

Amatriain, Xavier; Bonada, Jordi; Loscos, �lex; Arcos, Josep Lluís; Verfaille, Vincent

doi:10.1076/jnmr.32.1.95.16800

Cited by 23 publications

(12 citation statements)

References 10 publications

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“…(2) Socialbased approaches that characterize items based on textual attributes, cultural information, social tags and other kinds of web-based annotations [13,15,25]. (3) Content-based approaches that analyze the audio content for characterizing tracks and establishing itemto-item similarities [2]. (4) Collaborative Filtering (CF) methods that analyze listening patterns (or other sorts of feedback) by many users, in order to establish similarities across users and items.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This principle was successfully practiced in various matrix factorization techniques [10,18]. More specifically, now we map each item i into three distinct factor vectors p (1) i , p (2) i , p (3) i ∈ R , corresponding to the three distinct roles an item assumes: being recommended, annotating a station, and annotating a time window. Additionally, we tie the track and artist representation as before, by using the three summed vectors for track i: q…”

Section: Modeling New Usersmentioning

confidence: 99%

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Build your own music recommender by modeling internet radio streams

Aizenberg¹,

Koren²,

Somekh³

2012

Proceedings of the 21st International Conference on World Wide Web

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In the Internet music scene, where recommendation technology is key for navigating huge collections, large market players enjoy a considerable advantage. Accessing a wider pool of user feedback leads to an increasingly more accurate analysis of user tastes, effectively creating a "rich get richer" effect. This work aims at significantly lowering the entry barrier for creating music recommenders, through a paradigm coupling a public data source and a new collaborative filtering (CF) model. We claim that Internet radio stations form a readily available resource of abundant fresh human signals on music through their playlists, which are essentially cohesive sets of related tracks.In a way, our models rely on the knowledge of a diverse group of experts in lieu of the commonly used wisdom of crowds. Over several weeks, we aggregated publicly available playlists of thousands of Internet radio stations, resulting in a dataset encompassing millions of plays, and hundreds of thousands of tracks and artists. This provides the large scale ground data necessary to mitigate the cold start problem of new items at both mature and emerging services.Furthermore, we developed a new probabilistic CF model, tailored to the Internet radio resource. The success of the model was empirically validated on the collected dataset. Moreover, we tested the model at a cross-source transfer learning manner -the same model trained on the Internet radio data was used to predict behavior of Yahoo! Music users. This demonstrates the ability to tap the Internet radio signals in other music recommendation setups. Based on encouraging empirical results, our hope is that the proposed paradigm will make quality music recommendation accessible to all interested parties in the community.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Modeling New Usersmentioning

confidence: 99%

Build your own music recommender by modeling internet radio streams

Aizenberg¹,

Koren²,

Somekh³

2012

Proceedings of the 21st International Conference on World Wide Web

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“…Content-based processing is a new paradigm in digital audio processing that is based on symbolic or high-level manipulations of elements of a sound, rather than using signal processing alone (Amatriain et al, 2003). Lindsay, Parkes, and Fitzgerald (2003) propose context-sensitive effects that are more aware of the structure of the sound than current systems by utilising content descriptions such as those enabled by MPEG-7 (Thom, Purnhagen, Pfeiffer, & MPEG Audio Subgroup, 1999;Hunter, 1999).…”

Section: Content-based Processingmentioning

confidence: 99%

Concatenative sound synthesis: The early years

Schwarz

2006

Journal of New Music Research

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Concatenative sound synthesis is a promising method of musical sound synthesis with a steady stream of work and publications for over five years now. This article offers a comparative survey and taxonomy of the many different approaches to concatenative synthesis throughout the history of electronic music, starting in the 1950s, even if they weren't known as such at their time, up to the recent surge of contemporary methods. Concatenative sound synthesis methods use a large database of source sounds, segmented into units, and a unit selection algorithm that finds the units that match best the sound or musical phrase to be synthesised, called the target. The selection is performed according to the descriptors of the units. These are characteristics extracted from the source sounds, e.g. pitch, or attributed to them, e.g. instrument class. The selected units are then transformed to fully match the target specification, and concatenated. However, if the database is sufficiently large, the probability is high that a matching unit will be found, so the need to apply transformations is reduced. The most urgent and interesting problems for further work on concatenative synthesis are listed concerning segmentation, descriptors, efficiency, legality, data mining, and real time interaction. Finally, the conclusion tries to provide some insight into the current and future state of concatenative synthesis research.

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“…Using the perceptual categorization, audio effects are classified according to the most altered perceptual attribute: loudness, pitch, time, space, and timbre [5]. This classification is the most natural to musicians and audio listeners, since the perceptual attributes are clearly identified in music scores.…”

Section: A Classifications Of Digital Audio Effectsmentioning

confidence: 99%

Adaptive digital audio effects (a-DAFx): a new class of sound transformations

Verfaille

Zölzer

Arfib

2006

IEEE Trans. Audio Speech Lang. Process.

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After covering the basics of sound perception and giving an overview of commonly used audio effects (using a perceptual categorization), we propose a new concept called adaptive digital audio effects (A-DAFx). This consists of combining a sound transformation with an adaptive control. To create A-DAFx, low-level and perceptual features are extracted from the input signal, in order to derive the control values according to specific mapping functions. We detail the implementation of various new adaptive effects and give examples of their musical use.

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Content-based Transformations

Cited by 23 publications

References 10 publications

Build your own music recommender by modeling internet radio streams

Build your own music recommender by modeling internet radio streams

Concatenative sound synthesis: The early years

Adaptive digital audio effects (a-DAFx): a new class of sound transformations

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