Using concatenation cost for unit selection of homosonic segments in concatenative sound synthesis

This paper studies the issues surrounding the search and selection process in a general CSS system which may affect the synthesis result, namely the homosonic segments. Homosonic segments are first termed in this study, where it refers to audio files which have one or more of the same sonic properties with each other, but do not sound the same acoustically when played due to the limited audio features extracted during the analysis process. These homosonic segments create confusions within the CSS selection engine. This study proposes a robust solution to overcome this issue by introducing the concatenation cost in addition to the regular target cost. The experiment conducted in this study observes that the use of concatenation cost to help solve the problem is feasible. Further evaluation also suggests that the concatenation cost is an effective solution in solving the challenges involving homosonic segments as the sounds synthesised through concatenation cost function have a better accuracy and possess higher fluency when concatenated from one segment to the next

Word Embeddings for Automatic Equalization in Audio Mixing

In recent years, machine learning has been widely adopted to automate the audio mixing process. Automatic mixing systems have been applied to var- ious audio effects such as gain-adjustment, stereo panning, equalization, and reverberation. These systems can be controlled through visual interfaces, pro- viding audio examples, using knobs, and semantic descriptors. Using semantic descriptors or textual information to control these systems is an effective way for artists to communicate their creative goals. Furthermore, sometimes artists use non-technical words that may not be understood by the mixing system, or even a mixing engineer. In this paper, we explore the novel idea of using word embeddings to represent semantic descriptors. Word embeddings are generally obtained by training neural networks on large corpora of written text. These embeddings serve as the input layer of the neural network to create a trans- lation from words to EQ settings. Using this technique, the machine learning model can also generate EQ settings for semantic descriptors that it has not seen before. We perform experiments to demonstrate the feasibility of this idea. In addition, we compare the EQ settings of humans with the predictions of the neural network to evaluate the quality of predictions. The results showed that the embedding layer enables the neural network to understand semantic descrip- tors. We observed that the models with embedding layers perform better those without embedding layers, but not as good as human labels

Musicianship for Robots with Style

ABSTRACT In this paper we introduce a System conceived to serve as the "musical brain" of autonomous musical robots or agent-based software simulations of robotic systems. Our research goal is to provide robots with the ability to integrate with the musical culture of their surroundings. In a multi-agent configuration, the System can simulate an environment in which autonomous agents interact with each other as well as with external agents (e.g., robots, human beings or other systems). The main outcome of these interactions is the transformation and development of their musical styles as well as the musical style of the environment in which they live

Genetic Music System with Synthetic Biology

This paper introduces GeMS, a system for music composition informed by Synthetic Biology. GeMS generates music with simulations of genetic processes, such as transcription, translation and protein folding, with which biological systems render chains of amino acids from DNA strands. The system comprises the following components: the Miranda Machine, the Rhythmator and the Pitch Processor. The Miranda Machine is an abstract Turing Machine-like processor, which manipulates a sequence of DNA symbols according to a set of programming instructions. This process generates a pool of new DNA strands, which are subsequently translated into rhythms. GeMS represents the musical equivalent of amino acids in terms of rhythms, referred to as rhythmic codons. This enables the Rhythmator to convert DNA sequences into rhythmic sequences. The Pitch Processor generates pitches for such rhythmic sequences. It is inspired by the phenomenon of protein folding. The Pitch Processor considers orientation information of DNA instructions yielded by the Miranda Machine in order to activate algorithms for generating pitches. A musical composition, entitled Artibiotics, for percussion ensemble and electronic instruments, is presented to demonstrate the system