Pre-training Music Classification Models via Music Source Separation

In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks. To this end, we first pre-train U-Net networks under various music source separation objectives, such as the isolation of vocal or instrumental sources from a musical piece; afterwards, we attach a convolutional tail network to the pre-trained U-Net and jointly finetune the whole network. The features learned by the separation network are also propagated to the tail network through skip connections. Experimental results in two widely used and publicly available datasets indicate that pre-training the U-Nets with a music source separation objective can improve performance compared to both training the whole network from scratch and using the tail network as a standalone in two music classification tasks: music auto-tagging, when vocal separation is used, and music genre classification for the case of multi-source separation.Comment: 5 pages (4+references), 3 figures. ICASSP-24 submissio

Automated Composition of Picture-Synched Music Soundtracks for Movies

We describe the implementation of and early results from a system that automatically composes picture-synched musical soundtracks for videos and movies. We use the phrase "picture-synched" to mean that the structure of the automatically composed music is determined by visual events in the input movie, i.e. the final music is synchronised to visual events and features such as cut transitions or within-shot key-frame events. Our system combines automated video analysis and computer-generated music-composition techniques to create unique soundtracks in response to the video input, and can be thought of as an initial step in creating a computerised replacement for a human composer writing music to fit the picture-locked edit of a movie. Working only from the video information in the movie, key features are extracted from the input video, using video analysis techniques, which are then fed into a machine-learning-based music generation tool, to compose a piece of music from scratch. The resulting soundtrack is tied to video features, such as scene transition markers and scene-level energy values, and is unique to the input video. Although the system we describe here is only a preliminary proof-of-concept, user evaluations of the output of the system have been positive.Comment: To be presented at the 16th ACM SIGGRAPH European Conference on Visual Media Production. London, England: 17th-18th December 2019. 10 pages, 9 figure

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at https://salu133445.github.io/musegan/ .Comment: to appear at AAAI 201

Look, Listen and Learn

We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself -- the correspondence between the visual and the audio streams, and we introduce a novel "Audio-Visual Correspondence" learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art self-supervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks.Comment: Appears in: IEEE International Conference on Computer Vision (ICCV) 201

App creation in schools for different curricula subjects - lesson learned

The next generation of jobs will be characterized by an increased demand for people with computational and problem solving skills. In Austria, computer science topics are underrepresented in school curricula hence teaching time for these topics is limited. From primary through secondary school, only a few opportunities exist for young students to explore programming. Furthermore, today's teachers are rarely trained in computer science, which impairs their potential to motivate students in these courses. Within the "No One Left Behind" (NOLB) project, teachers were supported to guide and assist their students in their learning processes by constructing ideas through game making. Thus, students created games that referred to different subject areas by using the programming tool Pocket Code, an app developed at Graz University of Technology (TU-Graz). This tool helps students to take control of their own education, becoming more engaged, interested, and empowered as a result. To ensure an optimal integration of the app in diverse subjects the different backgrounds (technical and non-technical) of teachers must be considered as well. First, teachers were supported to use Pocket Code in the different subjects in school within the feasibility study of the project. Observed challenges and difficulties using the app have been gathered. Second, we conducted interviews with teachers and students to underpin our onsite observations. As a result, it was possible to validate Pocket Codes' potential to be used in a diverse range of subjects. Third, we focused especially on those teachers who were not technically trained to provide them with a framework for Pocket Code units, e.g., with the help of structured lesson plans and predefined templates.Comment: 10 pages, 5 tables EduLearn 201

End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss

Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query. The state-of-the-art approach to cross-modality retrieval relies on learning a joint embedding space of the two modalities, where items from either modality are retrieved using nearest-neighbor search. In this work, we introduce a neural network layer based on Canonical Correlation Analysis (CCA) that learns better embedding spaces by analytically computing projections that maximize correlation. In contrast to previous approaches, the CCA Layer (CCAL) allows us to combine existing objectives for embedding space learning, such as pairwise ranking losses, with the optimal projections of CCA. We show the effectiveness of our approach for cross-modality retrieval on three different scenarios (text-to-image, audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a multi-view network using freely learned projections optimized by a pairwise ranking loss, especially when little training data is available (the code for all three methods is released at: https://github.com/CPJKU/cca_layer).Comment: Preliminary version of a paper published in the International Journal of Multimedia Information Retrieva