Crossroads: Interactive Music Systems Transforming Performance, Production and Listening

date-added: 2017-12-22 18:26:58 +0000 date-modified: 2017-12-22 18:38:33 +0000 keywords: mood-based interaction, intelligent music production, HCI local-url: https://qmro.qmul.ac.uk/xmlui/handle/123456789/12502 publisher-url: http://mcl.open.ac.uk/music-chi/uploads/19/HCIMUSIC_2016_paper_15.pdf bdsk-url-1: https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/12502/Barthet%20Crossroads%3A%20Interactive%20Music%20Systems%202016%20Accepted.pdfdate-added: 2017-12-22 18:26:58 +0000 date-modified: 2017-12-22 18:38:33 +0000 keywords: mood-based interaction, intelligent music production, HCI local-url: https://qmro.qmul.ac.uk/xmlui/handle/123456789/12502 publisher-url: http://mcl.open.ac.uk/music-chi/uploads/19/HCIMUSIC_2016_paper_15.pdf bdsk-url-1: https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/12502/Barthet%20Crossroads%3A%20Interactive%20Music%20Systems%202016%20Accepted.pdfdate-added: 2017-12-22 18:26:58 +0000 date-modified: 2017-12-22 18:38:33 +0000 keywords: mood-based interaction, intelligent music production, HCI local-url: https://qmro.qmul.ac.uk/xmlui/handle/123456789/12502 publisher-url: http://mcl.open.ac.uk/music-chi/uploads/19/HCIMUSIC_2016_paper_15.pdf bdsk-url-1: https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/12502/Barthet%20Crossroads%3A%20Interactive%20Music%20Systems%202016%20Accepted.pdfWe discuss several state-of-the-art systems that propose new paradigms and user workflows for music composition, production, performance, and listening. We focus on a selection of systems that exploit recent advances in semantic and affective computing, music information retrieval (MIR) and semantic web, as well as insights from fields such as mobile computing and information visualisation. These systems offer the potential to provide transformative experiences for users, which is manifested in creativity, engagement, efficiency, discovery and affect

Feature Selection for Dynamic Range Compressor Parameter Estimation

date-added: 2018-05-07 00:06:23 +0000 date-modified: 2018-05-07 00:09:42 +0000 keywords: feature selection,. intelligent music production, AES, intelligent audio effects local-url: sheng2018aes.pdfdate-added: 2018-05-07 00:06:23 +0000 date-modified: 2018-05-07 00:09:42 +0000 keywords: feature selection,. intelligent music production, AES, intelligent audio effects local-url: sheng2018aes.pdfdate-added: 2018-05-07 00:06:23 +0000 date-modified: 2018-05-07 00:09:42 +0000 keywords: feature selection,. intelligent music production, AES, intelligent audio effects local-url: sheng2018aes.pdfCasual users of audio effects may lack practical experience or knowledge of their low-level signal processing parameters. An intelligent control tool that allows using sound examples to control effects would strongly benefit these users. In a previous work we proposed a control method for the dynamic range compressor (DRC) using a random forest regression model. It maps audio features extracted from a reference sound to DRC parameter values, such that the processed signal resembles the reference. The key to good performance in this system is the relevance and effectiveness of audio features. This paper focusses on a thorough exposition and assessment of the features, as well as the comparison of different strategies to find the optimal feature set for DRC parameter estimation, using automatic feature selection methods. This enables us to draw conclusions about which features are relevant to core DRC parameters. Our results show that conventional time and frequency domain features well known from the literature are sufficient to estimate the DRC’s threshold and ratio parameters, while more specialized features are needed for attack and release time, which induce more subtle changes to the signal

Variation in multitrack mixes : analysis of low-level audio signal features

To further the development of intelligent music production tools, towards generating mixes that would realistically be created by a human mix-engineer, it is important to understand what kind of mixes can be created, and are typically created, by human mix-engineers. This paper presents an analysis of 1501 mixes, over 10 different songs, created by mix-engineers. The primary dimensions of variation in the full dataset of mixes were ‘amplitude’, ‘brightness’, ‘bass’ and ‘width’, as determined by feature-extraction and subsequent principal component analysis. The distribution of representative features approximated a normal distribution and this is then used to obtain general trends and tolerance bounds for these features. The results presented here are useful as parametric guidance for intelligent music production systems

A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor

In this paper, a siamese DNN model is proposed to learn the characteristics of the audio dynamic range compressor (DRC). This facilitates an intelligent control system that uses audio examples to configure the DRC, a widely used non-linear audio signal conditioning technique in the areas of music production, speech communication and broadcasting. Several alternative siamese DNN architectures are proposed to learn feature embeddings that can characterise subtle effects due to dynamic range compression. These models are compared with each other as well as handcrafted features proposed in previous work. The evaluation of the relations between the hyperparameters of DNN and DRC parameters are also provided. The best model is able to produce a universal feature embedding that is capable of predicting multiple DRC parameters simultaneously, which is a significant improvement from our previous research. The feature embedding shows better performance than handcrafted audio features when predicting DRC parameters for both mono-instrument audio loops and polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201

Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles

This work is supported by China Scholarship Council (CSC) and EPSRC project (EP/L019981/1) Fusing Semantic and Audio Technologies for Intelligent Music Production and Consumption (FAST-IMPACt). Sandler acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award

Geolocation Adaptive Music Player

date-added: 2017-12-22 20:02:39 +0000 date-modified: 2017-12-22 20:05:50 +0000 keywords: adaptive music, intelligent music player, semantic audio, feature extraction bdsk-url-1: https://smartech.gatech.edu/bitstream/handle/1853/54586/WAC2016-47.pdfdate-added: 2017-12-22 20:02:39 +0000 date-modified: 2017-12-22 20:05:50 +0000 keywords: adaptive music, intelligent music player, semantic audio, feature extraction bdsk-url-1: https://smartech.gatech.edu/bitstream/handle/1853/54586/WAC2016-47.pdfWe present a web-based cross-platform adaptive music player that combines music information retrieval (MIR) and audio processing technologies with the interaction capabilities offered by GPS-equipped mobile devices. The application plays back a list of music tracks, which are linked to geographic paths in a map. The music player has two main enhanced features that adjust to the location of the user, namely, adaptable length of the songs and automatic transitions between tracks. Music tracks are represented as data packages containing audio and metadata (descriptive and behavioral) that builds on the concept of Digital Music Object (DMO). This representation, in line with nextgeneration web technologies, allows for exible production and consumption of novel musical experiences. A content provider assembles a data pack with music, descriptive analysis and action parameters that users can experience and control within the restrictions and templates defined by the provider

Approaches in Intelligent Music Production

Music production technology has made few advancements over the past few decades. State-of-the-art approaches are based on traditional studio paradigms with new developments primarily focusing on digital modelling of analog equipment. Intelligent music production (IMP) is the approach of introducing some level of artificial intelligence into the space of music production, which has the ability to change the field considerably. There are a multitude of methods that intelligent systems can employ to analyse, interact with, and modify audio. Some systems interact and collaborate with human mix engineers, while others are purely black box autonomous systems, which are uninterpretable and challenging to work with. This article outlines a number of key decisions that need to be considered while producing an intelligent music production system, and identifies some of the assumptions and constraints of each of the various approaches. One of the key aspects to consider in any IMP system is how an individual will interact with the system, and to what extent they can consistently use any IMP tools. The other key aspects are how the target or goal of the system is created and defined, and the manner in which the system directly interacts with audio. The potential for IMP systems to produce new and interesting approaches for analysing and manipulating audio, both for the intended application and creative misappropriation, is considerable

Technical Teaching for Violin of Associate Professor Dr. Kovit Kantasiri.

Abstract    The purpose of this research was to study the Associate professor Dr. Kovit Kantasiri’s violin teaching technique. This research was the qualitative research that the researchers interviewed and observed from Associate professor Dr. Kovit Kantasiri. According to the research, Associate professor Dr.Kovit techniques can be classified into 2 issues.    1. Associate professor Dr. Kovit used the child center method and chose the textbook which was suitable for each student. His teaching technique process was presentation, instruction, teaching conclusion and evaluation.    2. Associate professor Dr. Kovit taught his student to be proficient in Musical Intelligent. The student can use their Musicianship to test their violin technique in each topic that can be classified in 4 issues.    2.1 Technique of the left and right hand.    2.2 Tone Production and Pitch.    2.3 Musical and Style.    2.4 Combining the Musical Intelligent in high level.    According to the result of the research, Associate professor Dr. Kovit emphasize in ears training along with playing the instrument. The musician should be expert at music and can explain the music phenomenon. Knowing the music, the way he taught can help the student to develop their ability. Due to this, the Associate professor Dr. Kovit’s methods have a great profit to develop teaching process in Thailand.Keywords: Violin Teaching/Violin Techniques/Music Education