Titlebook: Computer Music Modeling and Retrieval; International Sympos Uffe Kock Wiil Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 20

Range-Of-Motion

掺假

把手

Conserving an Ancient Art of Music: Making SID Tunes Editable,on modern computers and thus conserves a music subculture of the eighties. So far, editing PSID files required working directly with Commodore 64 machine language. The paper gives a small overview of sound synthesis with the Commodore 64, argues why this topic is still interesting for both musicians

为宠爱

,“GMU” – An Integrated Microsound Synthesis System,he Max/MSP environment. GMU works in real time and its center is made of a few microsound synthesis objects (written in C at GMEM). Around these objects, we have elaborated sophisticated sound control strategies, with high-level interactive access inputs. They can be deterministics or stochastics. T

费解

What Are We Looking for?,we leave behind huge swathes of documents, which we will never describe or address due to lack of tools and time. At the same time humankind has produced and will continue producing unimaginable quantities of organised information through different media (photographs, books, music, television, radio

免除责任

同步左右

Brain-Imaging

Automatic Extraction of Approximate Repetitions in Polyphonic Midi Files Based on Perceptive Criterepetitions in a musical piece”. The algorithm that measures the repetitions relies on some general perceptive notions: it is non-linear, non-symetric and non-transitive. The model can analyse any music of any genre that contains a beat. The analysis is performed into three stages. First, we quantize

Grating

,Deriving Musical Structures from Signal Analysis for Music Audio Summary Generation: “Sequence” and. From the audio signal, we first derive features – static features (MFCC, chromagram) or proposed dynamic features. Two approaches are then studied in order to derive automatically the structure of a piece of music. The sequence approach considers the audio signal as a repetition of sequences of ev

Macronutrients

Musical Style Classification from Symbolic Data: A Two-Styles Case Study,on methods: Bayesian classifier, a .-NN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in t