An in-depth evaluation of multimodal video genre categorization

International audienceIn this paper we propose an in-depth evaluation of the performance of video descriptors to multimodal video genre categorization. We discuss the perspective of designing appropriate late fusion techniques that would enable to attain very high categorization accuracy, close to the one achieved with user-based text information. Evaluation is carried out in the context of the 2012 Video Genre Tagging Task of the MediaEval Benchmarking Initiative for Multimedia Evaluation, using a data set of up to 15.000 videos (3,200 hours of footage) and 26 video genre categories specific to web media. Results show that the proposed approach significantly improves genre categorization performance, outperforming other existing approaches. The main contribution of this paper is in the experimental part, several valuable interesting findings are reported that motivate further research on video genre classification

Combining audio-based similarity with web-based data to accelerate automatic music playlist generation

We present a technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently published interface for music players that benefits from intelligent structuring of audio collections. While the original approach involves the calculation of similarities between every pair of songs in a collection, we incorporate web-based data to reduce the number of necessary similarity calculations. More precisely, we exploit artist similarity determined automatically by means of web retrieval to avoid similarity calculation between tracks of dissimilar and/or unrelated artists. We evaluate our acceleration technique on two audio collections with different characteristics. It turns out that the proposed combination of audio- and text-based similarity not only reduces the number of necessary calculations considerably but also yields better results, in terms of musical quality, than the initial approach based on audio data only. Additionally, we conducted a small user study that further confirms the quality of the resulting playlists

On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique

International audienceThe traditional role of nearest-neighbor classification in musicclassification research is that of a straw man opponent for the learningapproach of the hour. Recent work in high-dimensional indexinghas shown that approximate nearest-neighbor algorithms are extremelyscalable, yielding results of reasonable quality from billions of high-dimensionalfeatures. With such efficient large-scale classifiers, the traditionalmusic classification methodology of reducing both feature dimensionalityand feature quantity is incorrect; instead the approximatenearest-neighbor classifier should be given an extensive data collectionto work with. We present a case study, using a well-known MIR classificationbenchmark with well-known music features, which shows thata simple nearest-neighbor classifier performs very competitively whengiven ample data. In this position paper, we therefore argue that nearest-neighborclassification has been treated unfairly in the literature and maybe much more competitive than previously thought

Addressing Tempo Estimation Octave Errors in Electronic Music by Incorporating Style Information Extracted From Wikipedia

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Digital humanism: The time is now

Digital humanism highlights the complex relationships between people, society, nature, and machines. It has been embraced by a growing community of individuals and groups who are setting directions that may change current paradigms. Here we focus on the initiatives generated by the Vienna Manifesto