Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe music box operad: Random generation of musical phrases from patterns
80
0
0.0
(
0
)
Authors
Samuele Giraudo
Ask ChatGPT about the research
No Arabic abstract
We introduce the notion of multi-pattern, a combinatorial abstraction of polyphonic musical phrases. The interest of this approach to encode musical phrases lies in the fact that it becomes possible to compose multi-patterns in order to produce new ones. This dives the set of musical phrases into an algebraic framework since the set of multi-patterns has the structure of an operad. Operads are algebraic structures offering a formalization of the notion of operators and their compositions. Seeing musical phrases as operators allows us to perform computations on phrases and admits applications in generative music. Indeed, given a set of short patterns, we propose various algorithms to randomly generate a new and longer phrase inspired by the inputted patterns.
rate research
Read More
We introduce the notion of multi-pattern, a combinatorial abstraction of polyphonic musical phrases. The interest of this approach lies in the fact that this offers a way to compose two multi-patterns in order to produce a longer one. This dives musical phrases into an algebraic context since the set of multi-patterns has the structure of an operad; operads being structures offering a formalization of the notion of operators and their compositions. Seeing musical phrases as operators allows us to perform computations on phrases and admits applications in generative music: given a set of short patterns, we propose various algorithms to randomly generate a new and longer phrase inspired by the inputted patterns.
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique `word, thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models.
We present in this paper PerformacnceNet, a neural network model we proposed recently to achieve score-to-audio music generation. The model learns to convert a music piece from the symbolic domain to the audio domain, assigning performance-level attributes such as changes in velocity automatically to the music and then synthesizing the audio. The model is therefore not just a neural audio synthesizer, but an AI performer that learns to interpret a musical score in its own way. The code and sample outputs of the model can be found online at https://github.com/bwang514/PerformanceNet.
Lyrics alignment in long music recordings can be memory exhaustive when performed in a single pass. In this study, we present a novel method that performs audio-to-lyrics alignment with a low memory consumption footprint regardless of the duration of the music recording. The proposed system first spots the anchoring words within the audio signal. With respect to these anchors, the recording is then segmented and a second-pass alignment is performed to obtain the word timings. We show that our audio-to-lyrics alignment system performs competitively with the state-of-the-art, while requiring much less computational resources. In addition, we utilise our lyrics alignment system to segment the music recordings into sentence-level chunks. Notably on the segmented recordings, we report the lyrics transcription scores on a number of benchmark test sets. Finally, our experiments highlight the importance of the source separation step for good performance on the transcription and alignment tasks. For reproducibility, we publicly share our code with the research community.
In recent years, music source separation has been one of the most intensively studied research areas in music information retrieval. Improvements in deep learning lead to a big progress in music source separation performance. However, most of the previous studies are restricted to separating a few limited number of sources, such as vocals, drums, bass, and other. In this study, we propose a network for audio query-based music source separation that can explicitly encode the source information from a query signal regardless of the number and/or kind of target signals. The proposed method consists of a Query-net and a Separator: given a query and a mixture, the Query-net encodes the query into the latent space, and the Separator estimates masks conditioned by the latent vector, which is then applied to the mixture for separation. The Separator can also generate masks using the latent vector from the training samples, allowing separation in the absence of a query. We evaluate our method on the MUSDB18 dataset, and experimental results show that the proposed method can separate multiple sources with a single network. In addition, through further investigation of the latent space we demonstrate that our method can generate continuous outputs via latent vector interpolation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
