AI THROUGHTHE EYES OF ORGANISE SOUND

ASEMINAR ON:

SUBMITTED TO:DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

INSTITUTE OF TECHNOLOGY AND MANAGEMENT, GIDA, GORAKHPUR

GUIDE: MR. NAFEES AKHTER FAROOQUI BY: JAIDEEP GHOSH

CONTENTS 1. Introduction

2. Approaches

3. Auto-Tune audio processor(AI technology)4. Applications

5. About the applications

6. Advantages 7. Disadvantages

8. Recent Research

9. Future: Planning and Learning

• Organised sound in AI

INTRODUCTION

WHAT IS AN ARTIFICIAL INTELLIGENCE?

John McCarthy(“father of AI”) [in 1956]

THE INTELLIGENCE OFMACHINESAND BRANCH OF COMPUTER SCIENCE THAT AIMS TO CREATE IT- “The study and design of intelligent agents”.

An area of computer science that deals with giving m/c ability to seem like they have human intelligence.

• Organised Sound was founded in 1996.

• Artificial intelligence is a rich and still-developing field with many musical applications. This article surveys the use of artificial intelligence approaches in the pages of Organised Sound.

• Organised Sound is well placed as the leading platform for electroacoustic composers, sound designers, sonic artists, etc.

• We can define music as organised sound. It can be organized melodically, rhythmically, and harmonically.

• The sound waves for musical sounds are organised.

• Organised Sound is an international peer- reviewed academic journal which focuses on the rapidly developing methods & issues arising from the use of technology in music today.

ORGANISED SOUND IN AI

1. Phenomenological approach

2. An ’ action- sound ‘ approach to teaching music technology

3. Ear Sketch-Integrated approach

4. Diadactical approach to Electracoustic musics

APPROACHES

For correcting pitch in general, see Pitch correction. Auto-Tune is an audioprocessor created by Antares Audio Technologies

which uses a proprietary device to measure and alter pitch in vocal and instrumental music recording and performances.

The processor slightly shifts pitches to the nearest true semitone (to the exact pitch of the nearest tone in traditional equal temperament). Auto-Tune can also be used as an effect to distort the human voice when pitch is raised or lowered significantly.

Figure: How Auto-Tune audio processor works

Auto-Tune has become standard equipment in professional recording studios.

Instruments like the Peavey AT-200 guitar are seamlessly using the Auto Tune technology for real time pitch correction.

Auto-Tune was designed to discreetly correct imprecise intonations.

Figure: Auto-Tune audio processor

APPLICATIONS General sound classification & similarity In MPEG-7

Open sound control: an enabling technology For musical networking

A review of time- frequency representations, with application to sound music analysis - resynthesis

We introduce a system for generalized sound classification and similarity using a machine-learning framework.

Applications of the system include automatic classification of

environmental sounds, musical instruments, music genre and human speakers. In addition to classification, the system may also be used for computing similarity metrics between a target sound and other sounds in a database.

We discuss the use of hidden Markov models for representing the temporal evolution of audio spectra and present results of testing the system on classification and retrieval tasks.

The system has been incorporated into the MPEG-7 international standard for multimedia content description and is therefore publicly available in the form of a set of standardized interfaces and software reference tools for developers and researchers.

GENERAL SOUND CLASSIFICATION AND SIMILARITYIN MPEG-7

Since telecommunication can never equal the richness of face-to-face interaction on its own terms, the most interesting examples of networked music go beyond the paradigm of musicians playing together in a virtual room.

The Open Sound Control protocol has facilitated dozens of such innovative networked music projects.

First the protocol itself is described, followed by some theoretical limits communication latency and what they mean for music making.

Then a representative list of some of the projects that take advantage of the protocol is presented, describing each project in terms of the paradigm of musical interaction that it provides.

OPEN SOUND CONTROL:AN ENABLING TECHNOLOGY FOR MUSICAL NETWORKING

Analysis– resynthesis (A–R) systems gain their flexibility for creative transformation of sound by representing sound as a set of musically useful features.

The analysis process extracts these features from the time domain signal by means of a time–frequency representation (TFR).

The TFR provides an intermediate representation of sound that must make

the features accessible and measurable to the rest of the analysis.

This paper reviews these contemporary approaches in comparison with the more classical ones and with reference to their applicability, merits and shortcomings for application to sound analysis.

FREQUENCY REPRESENTATIONS, WITH APPLICATION TO SOUND MUSIC ANALYSIS

• Second point, music can make us feel more comfortable.

• First point, the music can make us feel relieve.

• Third point, music can reduce your stresses all the time.

• This is the last point of listening music, music can bring us in to one society by the meaning of the song.

ADVANTAGES

Third point, music influences the human behavior.

First point, creating a noise pollution.

Second point, technologies which are used to moderate Frequency are very expensive.

DISADVANTAGE

The rest of this article surveys four areas of activity that apply artificial intelligence concepts and techniques to music:

The intelligent composer’s assistant. Responsive instruments. Recognition and understanding of musical sound.

RECENT REASEARCH

The Intelligent Composer’s Assistant

As the use of interactive graphics techniques begins to enter the computer music domain, there is a corresponding need to rethink carefully the discourse between the musician and the m/c.The overall goal of an intelligent composer’s assistant is to support the composer in highly creative phases of composition. This includes the creation of the plan and architecture of a composition, and the encoding of musical material into the working score.

Recognition and Understanding of Musical Sound: The initial impetus in intelligentA main goal of intelligent musical signal processing is building systems that listen to and understand music by relating it to previously stored knowledge. The initial impetus in intelligent signal processing came from speech-understanding research . In signal processing, the distinction between analysis and understanding can be explained as follows. Signal analysis attempts to segment and label an inputstream according to preset categories.

THE FUTURE: PLANNING AND LEARNING

Planning, as usually defined in AI research, is intimately linked with establishing problem- solving strategies. In a problem solver, planning involves setting subgoals dynamically and being able to backtrack and revise subgoals when an attempt to solve a problem fails. Carrying out any substantial musical task involves planning. For example, instrumentalists make plans for playing apiece by rehearsing and planning optimal Fingerings.

Planning in Music Systems

Learning in Music Systems

Learning has been defined as the process by which a human being or a machine increases its knowledge and improves its Skills. A great deal of AI work has focused on learning by example, so it is not surprising that initial studies in music learning by machine also follow this approach.

CONCLUSIONThis topic is totally related to AI through organised sound. The AI technologies Used to create or made a organised sound in which there is no irregularities. Also many AI technologies are used to moderate frequency and correct the Pitch in plan and organised sound.

REFERENCES

[1.] Blackwell, T., & Young, M. (2004). Self-organised music. Organised Sound, 9(02). doi:10.1017/S1355771804000214

[2.] Bourotte, R., & Delhaye, C. (2013). Learn to Think for Yourself: Impelled by UPIC to open new ways of composing. Organised Sound, 18(02), 134–145. doi:10.1017/S1355771813000058

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