MUSIC GENERATION USING DEEP LEARNING

We examine how lengthy short-term memory neural networks (NNs) may be utilised to create music compositions and offer a method for doing so in this study. Bach's musical style was chosen to train the NN in order for it to make similar music works. The recommended method converts midi files to song files before encoding them as NN inputs. Before feeding the files into the NNs, they are augmented, which converts them into distinct keys, and then they are fed into the NN for training. The final phase is the creation of music. The primary purpose is to assign an arbitrary note to the NN, which it will gradually modify until it produces a good piece of music. Several tests have been conducted in order to identify the ideal parameter values for producing good music. Keywords: lstm, music generation, deep learning, machine learning, neural networks

Algorithmic music generation by harmony recombination with genetic algorithm

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.

MCMA: A Symbolic Multitrack Contrapuntal Music Archive

We present Multitrack Contrapuntal Music Archive (MCMA, available at https://mcma.readthedocs.io), a symbolic dataset of pieces specifically curated to comprise, for any given polyphonic work, independent voices. So far, MCMA consists only of pieces from the Baroque repertoire but we aim to extend it to other contrapuntal music. MCMA is FAIR-compliant and it is geared towards musicological tasks such as (computational) analysis or education, as it brings to the fore contrapuntal interactions by explicit and independent representation. Furthermore, it affords for a more apt usage of recent advances in the field of natural language processing (e.g., neural machine translation). For example, MCMA can be particularly useful in the context of language-based machine learning models for music generation. Despite its current modest size, we believe MCMA to be an important addition to online contrapuntal music databases, and we thus open it to contributions from the wider community, in the hope that MCMA can continue to grow beyond our efforts. In this article, we provide the rationale for this corpus, suggest possible use cases, offer an overview of the compiling process (data sourcing and processing), and present a brief statistical analysis of the corpus at the time of writing. Finally, future work that we endeavor to undertake is discussed.

The Ai Music Generation Challenge 2020: Double Jigs in the Style of O'Neill's ``1001''

The Ai Music Generation Challenge 2020 had three objectives: 1) to promote meaningful approaches to evaluating artificial intelligence (Ai) applied to music;2) to see how music Ai research can benefit from considering traditional music, and how traditional music might benefit from music Ai research; and 3)to facilitate discussions about the ethics of music Ai research applied to traditional music practices.There were six participants and a benchmark in the challenge, each competing to build an artificial system that generates the most plausible double jigs, as judged against the 365 published in solved'', but that the evaluation of such systems can be done in meaningful ways.The article ends by reflecting on the challenge and considering the coming 2021 challenge.