6,425 research outputs found
Neural networks for musical chords recognition
peer reviewedIn this paper, we consider the challenging problem of music recognition and present an effective machine learning based method using a feed-forward neural network for chord recognition. The method uses the known feature vector for automatic chord recognition called the Pitch Class Profile (PCP). Although the PCP vector only provides attributes corresponding to 12 semi-tone values, we show that it is adequate for chord recognition.
Part of our work also relates to the design of a database of chords. Our database is primarily designed for chords typical of Western Europe music. In particular, we have built a large dataset filled with recorded guitar chords under different acquisition conditions (instruments, microphones, etc), but also with samples obtained with other instruments. Our experiments establish a twofold result: (1) the PCP is well suited for describing chords in a machine learning context, and (2) the algorithm is also capable to recognize chords played with other instruments, even unknown from the training phase
Pengenalan Chord pada Alat Musik Gitar Menggunakan CodeBook dengan Teknik Ekstraksi Ciri MFCC
Human auditory system is capable of extracting rich and meaningful data from complex audio signal. To recognize chord sequences that played in some kind of music is not an easy task. People need big effort to train their sense of hearing so they can recognize that kind sound of chords. This condition is also valid in a computer system. Finding the key and labeling the chords automatically from music are great use for those who want to do harmonic analysis of music. Hence automatic chord recognition has been a topic of interest in the context of Music Information Retrieval (MIR) for several years, and attempts have been made in implementing such systems using well understood signal processing and pattern recognition techniques. This research is about to recognize the sound of chord that played and recorded by guitar instrument. There are 24 major-minor chords that used in this research. MFCC is used as feature extraction and the number of coefficient cepstral that used are 13 and 26. Each chord signal that has been extracted then clustered using K-means algorithm with 8, 12, 16, 20, 24, 28, 32 k numbers to create codebook that use as a model of each chord. For the recognition process, there are two methods that used in this research, unstructured recognition and structured recognition. For the result, this research produces two kinds model of codebook that are codebook with 13 coefficients and codebook with 26 coefficients. Both types of codebook show a good result with accuracy level above 88%. The best result yielded from USAge of 26 coefficient cepstral with structured recognition. It's accuracy level reach 97%. Hence the USAge of 26 coefficient cepstral is better than the USAge of 13 coefficient cepstral with difference of accuration level is about 7%. This research also shows the affectation of the numbers k-means that used. An increasing accuration level shown by increasing the amount of k-cluster
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
Graphs with Plane Outside-Obstacle Representations
An \emph{obstacle representation} of a graph consists of a set of polygonal
obstacles and a distinct point for each vertex such that two points see each
other if and only if the corresponding vertices are adjacent. Obstacle
representations are a recent generalization of classical polygon--vertex
visibility graphs, for which the characterization and recognition problems are
long-standing open questions.
In this paper, we study \emph{plane outside-obstacle representations}, where
all obstacles lie in the unbounded face of the representation and no two
visibility segments cross. We give a combinatorial characterization of the
biconnected graphs that admit such a representation. Based on this
characterization, we present a simple linear-time recognition algorithm for
these graphs. As a side result, we show that the plane vertex--polygon
visibility graphs are exactly the maximal outerplanar graphs and that every
chordal outerplanar graph has an outside-obstacle representation.Comment: 12 pages, 7 figure
On the Mathematics of Music: From Chords to Fourier Analysis
Mathematics is a far reaching discipline and its tools appear in many
applications. In this paper we discuss its role in music and signal processing
by revisiting the use of mathematics in algorithms that can extract chord
information from recorded music. We begin with a light introduction to the
theory of music and motivate the use of Fourier analysis in audio processing.
We introduce the discrete and continuous Fourier transforms and investigate
their use in extracting important information from audio data
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