microsoft audio processing music classification

Multi-Task Learning for Multiple Language Translation.

Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang.

In Proceedings of the AISB 2003 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences. Music generation from statistical models.

In Proceedings of the 25th international conference on Machine learning. A unified architecture for natural language processing: Deep neural networks with multitask learning. Song from pi: A musically plausible network for pop music generation.

Hang Chu, Raquel Urtasun, and Sanja Fidler.

Predictive tabla modelling using variable-length markov and hidden markov models.

Parag Chordia, Avinash Sastry, and Sertan cSentürk.

On the properties of neural machine translation: Encoder-decoder approaches.

Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio.

Magenta: An architecture for real time automatic composition of background music International Workshop on Intelligent Virtual Agents. A Unit Selection Methodology for Music Generation Using Deep Neural Networks.

Mason Bretan, Gil Weinberg, and Larry Heck.

Large-scale machine learning with stochastic gradient descent. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music.

Microsoft audio processing music classification