Name
Abstract | ||
|---|---|---|
Computer dialogue systems are designed with the intention of supporting meaningful interactions with humans. Common modes of communication include speech, text, and physical gestures. In this work we explore a communication paradigm in which the input and output channels consist of music. Specifically, we examine the musical interaction scenario of call and response. We present a system that utilizes a deep autoencoder to learn semantic embeddings of musical input. The system learns to transform these embeddings in a manner such that reconstructing from these transformation vectors produces appropriate musical responses. In order to generate a response the system employs a combination of generation and unit selection. Selection is based on a nearest neighbor search within the embedding space and for real-time application the search space is pruned using vector quantization. The live demo consists of a person playing a midi keyboard and the computer generating a response that is played through a loudspeaker. |
Year | Venue | Field |
|---|---|---|
2017 | THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | New Interfaces for Musical Expression,Music and emotion,Musical,Computer science,Cognitive science,Musical composition,Music,Popular music,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mason Bretan | 1 | 9 | 2.93 |
| Sageev Oore | 2 | 101 | 18.63 |
| Jesse H. Engel | 3 | 326 | 20.21 |
| Douglas Eck | 4 | 748 | 64.84 |
| Larry P. Heck | 5 | 1096 | 100.58 |