Introduction to Intelligent Music Systems and Applications

Schedl, Markus; Yang, Yi-Hsuan; Herrera, Perfecto

doi:10.1145/2991468

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…In the pre-training stage, noisy speech was used to train Restricted Bolzmann Machines (RBMs) layer-by-layer in a standard unsupervised greedy fashion to obtain a deep generative model [41]; whereas, in the fine-tuning process, the desired clean speech was set as the target by minimising the objective function as Eq. (22). Similar research efforts were also extensively made on the log magnitude [63] and the log-Mel-spectral domains [36], respectively.…”

Section: A Mapping-based Deep Enhancement Methodsmentioning

confidence: 96%

Deep Learning for Environmentally Robust Speech Recognition

Zhang

Geiger

Pohjalainen

et al. 2018

ACM Trans. Intell. Syst. Technol.

293

144

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Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single-and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

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Section: A Mapping-based Deep Enhancement Methodsmentioning

confidence: 96%

Deep Learning for Environmentally Robust Speech Recognition

Zhang

Geiger

Pohjalainen

et al. 2018

ACM Trans. Intell. Syst. Technol.

293

144

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“…Schedl, Yang, and Herrera-Boyer define the canon of music systems and applications that are utilized to automate certain music production or music selection processes for online stores and streaming services as intelligent technologies, while also acknowledging the problems in defining something as intelligent (Schedl et al 2016). As there is no definitive definition for what intelligence entails, the word is used more often as a marketing technique than a description of what a product/software/platform can do, functionally speaking.…”

Section: Situating Artificial Intelligence Popular Musicmentioning

confidence: 99%

“…. to generate sequences that other systems consider as 'music,' or to anticipate musical features or events while listening to a musical performance, or all of them at the same time" (Schedl et al 2016). They therefore build a typology of examples of intelligent music systems, which includes: systems for automatic music composition and creation, such as CHORAL, Iamus, Coninuator, OMax; systems to predict music listening, such as Just-For-Me, and Mobile Music Genius; systems for music discovery, such as Musicream; and algorithms to curate music based on mood/emotions.…”

Section: Situating Artificial Intelligence Popular Musicmentioning

confidence: 99%

“…There is a much longer history of artificial intelligence music in the art music tradition (Miranda 2000;Schedl et al 2016;Roads 1980), but its use in popular music is currently predominantly that of novelty, experimentation, and largely as a tool for collaboration. The purpose of this article, therefore, is to document the present practice and understanding of the use of artificial intelligence in popular music, focusing on the first human-AI collaborated album, Hello World, by SKYGGE, utilizing the Flow Machines technology and led by François Pachet and Benoît Carré. Using SKYGGE as a case study creates space to discuss some of the current perceptions, fears, and benefits of AI pop music, and builds on the theoretical discussions surrounding AIM (artificial intelligence music) that have occurred with the more Avant Garde uses of these technologies.…”

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confidence: 99%

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Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

Avdeeff

2019

Arts

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This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.

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“…For example, according to Nelsen market report, on-demand song streaming volume is up 45%, having already exceeded 268 billion in 2018. In response to the needs for tools to fast access such large size of music information, different kinds of indexing methods have been recently proposed to support efficient content-based music information retrieval (CBMIR) and analysis during the last decades [8][9][10][11][12][13][14]. The specific examples include the CM*F [15], QUC-tree [16], LSH-based approaches [17][18][19] and so on.…”

Section: Introductionmentioning

confidence: 99%

Toward efficient indexing structure for scalable content-based music retrieval

Shen

Mei

et al. 2019

Multimedia Systems

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With advancement of various information processing and storage techniques, the scale of digital music collections has been growing at very fast speed during recent decades. To support high-quality content-based retrieval over such a large volume of music data, how to develop indexing structure with good effectiveness, efficiency and scalability becomes an important research issue. However, existing techniques mainly focus on improving query efficiency. Very few approaches have been proposed to address issues related to scalability and accuracy. In this study, we address the problem via introducing a novel indexing technique called effective music indexing framework (EMIF) to facilitate scalable and accurate music retrieval. It is designed based on a "classification-and-indexing" principle and consists of two main functionality modules: (1) music classification-a novel semantic-sensitive classification to identify an input song's category and (2) indexing modulemultiple local indexing structures, one for each semantic category to reduce query response time significantly. In particular, the classification model combining linear discriminative mixture model (LDMM) and advanced score fusion scheme has been applied to estimate category of music accurately. Layered architecture enables EMIF to enjoy superior scalability and efficiency. To evaluate the approach, a set of experimental studies has been carried out using two large music test collections and the results demonstrate various advantages of EMIF over state-of-the-art approaches including efficiency, scalability and effectiveness.

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Introduction to Intelligent Music Systems and Applications

Cited by 6 publications

References 18 publications

Deep Learning for Environmentally Robust Speech Recognition

Deep Learning for Environmentally Robust Speech Recognition

Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

Toward efficient indexing structure for scalable content-based music retrieval

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