Content-based music classification, summarization and retrieval

Ph.DDOCTOR OF PHILOSOPH

Final Research Report on Auto-Tagging of Music

The deliverable D4.7 concerns the work achieved by IRCAM until M36 for the “auto-tagging of music”. The deliverable is a research report. The software libraries resulting from the research have been integrated into Fincons/HearDis! Music Library Manager or are used by TU Berlin. The final software libraries are described in D4.5. The research work on auto-tagging has concentrated on four aspects: 1) Further improving IRCAM’s machine-learning system ircamclass. This has been done by developing the new MASSS audio features, including audio augmentation and audio segmentation into ircamclass. The system has then been applied to train HearDis! “soft” features (Vocals-1, Vocals-2, Pop-Appeal, Intensity, Instrumentation, Timbre, Genre, Style). This is described in Part 3. 2) Developing two sets of “hard” features (i.e. related to musical or musicological concepts) as specified by HearDis! (for integration into Fincons/HearDis! Music Library Manager) and TU Berlin (as input for the prediction model of the GMBI attributes). Such features are either derived from previously estimated higher-level concepts (such as structure, key or succession of chords) or by developing new signal processing algorithm (such as HPSS) or main melody estimation. This is described in Part 4. 3) Developing audio features to characterize the audio quality of a music track. The goal is to describe the quality of the audio independently of its apparent encoding. This is then used to estimate audio degradation or music decade. This is to be used to ensure that playlists contain tracks with similar audio quality. This is described in Part 5. 4) Developing innovative algorithms to extract specific audio features to improve music mixes. So far, innovative techniques (based on various Blind Audio Source Separation algorithms and Convolutional Neural Network) have been developed for singing voice separation, singing voice segmentation, music structure boundaries estimation, and DJ cue-region estimation. This is described in Part 6.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D

Visual to Sound: Generating Natural Sound for Videos in the Wild

As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlated during natural events, these two modalities combine to jointly affect human perception. In this paper, we pose the task of generating sound given visual input. Such capabilities could help enable applications in virtual reality (generating sound for virtual scenes automatically) or provide additional accessibility to images or videos for people with visual impairments. As a first step in this direction, we apply learning-based methods to generate raw waveform samples given input video frames. We evaluate our models on a dataset of videos containing a variety of sounds (such as ambient sounds and sounds from people/animals). Our experiments show that the generated sounds are fairly realistic and have good temporal synchronization with the visual inputs.Comment: Project page: http://bvision11.cs.unc.edu/bigpen/yipin/visual2sound_webpage/visual2sound.htm

Automated generation of movie tributes

O objetivo desta tese é gerar um tributo a um filme sob a forma de videoclip, considerando como entrada um filme e um segmento musical coerente. Um tributo é considerado um vídeo que contém os clips mais significativos de um filme, reproduzidos sequencialmente, enquanto uma música toca. Nesta proposta, os clips a constar do tributo final são o resultado da sumarização das legendas do filme com um algoritmo de sumarização genérico. É importante que o artefacto seja coerente e fluido, pelo que há a necessidade de haver um equilíbrio entre a seleção de conteúdo importante e a seleção de conteúdo que esteja em harmonia com a música. Para tal, os clips são filtrados de forma a garantir que apenas aqueles que contêm a mesma emoção da música aparecem no vídeo final. Tal é feito através da extração de vetores de características áudio relacionadas com emoções das cenas às quais os clips pertencem e da música, e, de seguida, da sua comparação por meio do cálculo de uma medida de distância. Por fim, os clips filtrados preenchem a música cronologicamente. Os resultados foram positivos: em média, os tributos produzidos obtiveram 7 pontos, numa escala de 0 a 10, em critérios como seleção de conteúdo e coerência emocional, fruto de avaliação humana.This thesis’ purpose is to generate a movie tribute in the form of a videoclip for a given movie and music. A tribute is considered to be a video containing meaningful clips from the movie playing along with a cohesive music piece. In this work, we collect the clips by summarizing the movie subtitles with a generic summarization algorithm. It is important that the artifact is coherent and fluid, hence there is the need to balance between the selection of important content and the selection of content that is in harmony with the music. To achieve so, clips are filtered so as to ensure that only those that contain the same emotion as the music are chosen to appear in the final video. This is made by extracting vectors of emotion-related audio features from the scenes they belong to and from the music, and then comparing them with a distance measure. Finally, filtered clips fill the music length in a chronological order. Results were positive: on average, the produced tributes obtained scores of 7, on a scale from 0 to 10, on content selection, and emotional coherence criteria, from human evaluation

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure