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"music generation": models, code, and papers

Live Orchestral Piano, a system for real-time orchestral music generation

May 18, 2017
Léopold Crestel, Philippe Esling

This paper introduces the first system for performing automatic orchestration based on a real-time piano input. We believe that it is possible to learn the underlying regularities existing between piano scores and their orchestrations by notorious composers, in order to automatically perform this task on novel piano inputs. To that end, we investigate a class of statistical inference models called conditional Restricted Boltzmann Machine (cRBM). We introduce a specific evaluation framework for orchestral generation based on a prediction task in order to assess the quality of different models. As prediction and creation are two widely different endeavours, we discuss the potential biases in evaluating temporal generative models through prediction tasks and their impact on a creative system. Finally, we introduce an implementation of the proposed model called Live Orchestral Piano (LOP), which allows to perform real-time projective orchestration of a MIDI keyboard input.

  

Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging

Jan 30, 2021
Andres Ferraro, Yuntae Kim, Soohyeon Lee, Biho Kim, Namjun Jo, Semi Lim, Suyon Lim, Jungtaek Jang, Sehwan Kim, Xavier Serra, Dmitry Bogdanov

One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks and 148,826 associated playlists annotated by 30,652 different tags. All the data is gathered from Melon, a popular Korean streaming service. The dataset is suitable for music information retrieval tasks, in particular, auto-tagging and automatic playlist continuation. Even though the latter can be addressed by collaborative filtering approaches, audio provides opportunities for research on track suggestions and building systems resistant to the cold-start problem, for which we provide a baseline. Moreover, the playlists and the annotations included in the Melon Playlist Dataset make it suitable for metric learning and representation learning.

* 2021 IEEE International Conference on Acoustics, Speech and Signal Processing 
  

Spectrogram Inpainting for Interactive Generation of Instrument Sounds

Apr 15, 2021
Théis Bazin, Gaëtan Hadjeres, Philippe Esling, Mikhail Malt

Modern approaches to sound synthesis using deep neural networks are hard to control, especially when fine-grained conditioning information is not available, hindering their adoption by musicians. In this paper, we cast the generation of individual instrumental notes as an inpainting-based task, introducing novel and unique ways to iteratively shape sounds. To this end, we propose a two-step approach: first, we adapt the VQ-VAE-2 image generation architecture to spectrograms in order to convert real-valued spectrograms into compact discrete codemaps, we then implement token-masked Transformers for the inpainting-based generation of these codemaps. We apply the proposed architecture on the NSynth dataset on masked resampling tasks. Most crucially, we open-source an interactive web interface to transform sounds by inpainting, for artists and practitioners alike, opening up to new, creative uses.

* Proceedings of the 1st Joint Conference on AI Music Creativity, 2020 (p. 10). Stockholm, Sweden: AIMC 
* 8 pages + references + appendices. 4 figures. Published as a conference paper at the The 2020 Joint Conference on AI Music Creativity, October 19-23, 2020, organized and hosted virtually by the Royal Institute of Technology (KTH), Stockholm, Sweden 
  

Learning Temporal Dependencies in Data Using a DBN-BLSTM

Dec 23, 2014
Kratarth Goel, Raunaq Vohra

Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network - Bidirectional Long Short-Term Memory (DBN-BLSTM) network that models sequences by keeping track of the temporal information while enabling deep representations in the data. We demonstrate this new architecture by applying it to the task of music generation and obtain state-of-the-art results.

* 6 pages, 2 figures, 1 table, ICLR 2015 conference track submission under review 
  

A Survey on Audio Synthesis and Audio-Visual Multimodal Processing

Aug 01, 2021
Zhaofeng Shi

With the development of deep learning and artificial intelligence, audio synthesis has a pivotal role in the area of machine learning and shows strong applicability in the industry. Meanwhile, significant efforts have been dedicated by researchers to handle multimodal tasks at present such as audio-visual multimodal processing. In this paper, we conduct a survey on audio synthesis and audio-visual multimodal processing, which helps understand current research and future trends. This review focuses on text to speech(TTS), music generation and some tasks that combine visual and acoustic information. The corresponding technical methods are comprehensively classified and introduced, and their future development trends are prospected. This survey can provide some guidance for researchers who are interested in the areas like audio synthesis and audio-visual multimodal processing.

  

A Mood-based Genre Classification of Television Content

Aug 06, 2015
Humberto Corona, Michael P. O'Mahony

The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts. We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets. Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation. This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.

* in ACM Workshop on Recommendation Systems for Television and Online Video 2014 Foster City, California USA 
  

Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time

Jul 23, 2019
Kārlis Freivalds, Emīls Ozoliņš, Agris Šostaks

A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O($n^2$) complexity that leads to slow execution for long sequences. We introduce a new Shuffle-Exchange neural network model for sequence to sequence tasks which have O(log n) depth and O(n log n) total complexity. We show that this model is powerful enough to infer efficient algorithms for common algorithmic benchmarks including sorting, addition and multiplication. We evaluate our architecture on the challenging LAMBADA question answering dataset and compare it with the state-of-the-art models which use attention. Our model achieves competitive accuracy and scales to sequences with more than a hundred thousand of elements. We are confident that the proposed model has the potential for building more efficient architectures for processing large interrelated data in language modeling, music generation and other application domains.

  

Delivering Gigabit Capacities to Passenger Trains Tales from an Operator on the Road to 5G

May 14, 2021
Nima Jamaly, Stefan Mauron, Ruben Merz, Adrian Schumacher, Daniel Wenger

Delivering reliable and high-capacity Internet connectivity to high-speed train users is a challenge. Modern railway cars act as Faraday cages and a typical train consist comprises several hundreds of users moving at high velocity. Furthermore, with the global availability of fourth generation (4G) Long Term Evolution (LTE), user expectations have dramatically increased: it is expected to be online anytime and anywhere. Demand for mobile high-capacity is being driven by video and music streaming services, for lower latency and higher availability by gaming, and for more reliability and even uplink capacity by mission critical applications. Finally, the life-cycle of the railway industry is much longer than for telecommunications, which makes supporting 5G challenging. In this paper, we survey the challenges associated with delivering high-capacity connectivity to train users, describe potential options, and highlight how a leading western European operator is tackling these challenges and preparing for 5G and beyond.

* IEEE Communications Magazine (Volume: 57, Issue: 9, September 2019) 
* Published in IEEE Communications Magazine (Volume: 57, Issue: 9, September 2019) 
  

Hallmarks of Human-Machine Collaboration: A framework for assessment in the DARPA Communicating with Computers Program

Feb 09, 2021
Robyn Kozierok, John Aberdeen, Cheryl Clark, Christopher Garay, Bradley Goodman, Tonia Korves, Lynette Hirschman, Patricia L. McDermott, Matthew W. Peterson

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified "Hallmarks" of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.

* 20 pages, 21 figures 
  

Differentiable Time-Frequency Scattering in Kymatio

Apr 19, 2022
John Muradeli, Cyrus Vahidi, Changhong Wang, Han Han, Vincent Lostanlen, Mathieu Lagrange, George Fazekas

Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales. It offers an idealized model of spectrotemporal receptive fields (STRF) in the primary auditory cortex, and thus may serve as a biological plausible surrogate for human perceptual judgments at the scale of isolated audio events. Yet, prior implementations of JTFS and STRF have remained outside of the standard toolkit of perceptual similarity measures and evaluation methods for audio generation. We trace this issue down to three limitations: differentiability, speed, and flexibility. In this paper, we present an implementation of time-frequency scattering in Kymatio, an open-source Python package for scattering transforms. Unlike prior implementations, Kymatio accommodates NumPy and PyTorch as backends and is thus portable on both CPU and GPU. We demonstrate the usefulness of JTFS in Kymatio via three applications: unsupervised manifold learning of spectrotemporal modulations, supervised classification of musical instruments, and texture resynthesis of bioacoustic sounds.

* 8 pages, 6 figures. Submitted to the International Conference on Digital Audio Effects (DAFX) 2022 
  
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