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Home Program TPC Committees Authors Other reviewers
Committees
Steering Committee
A Adiwijaya (Telkom University, Indonesia) Ahmad Rafi (Multimedia University, Malaysia)
Afizan Azman (Melaka International College of Science and Technology Malaysia) Ari Moesriami Barmawi (Telkom University, Indonesia)
Hairul A. Abdul-Rashid (Multimedia University, Malaysia) Siong Hoe Lau (Multimedia University, Malaysia)
Maman Abdurohman (Telkom University, Indonesia) Parman Sukarno (Telkom University, Indonesia) Rina Pudjiastuti (Telkom University, Indonesia) Shafinar Ismail (Universiti Teknologi Mara, Malaysia)
Syed Abdul Rahman Al Haddad (Universiti Putra Malaysia, Malaysia) Kiki Maulana Adhinugraha (La Trobe University, Australia)
Sultan Alamri (SEU, Saudi Arabia)
Conference Committee
General Chair
Ema Rachmawati (Telkom University, Indonesia)
General Co-Chair
Warih Maharani (Telkom University, Indonesia) Ong Thian Song (Multimedia University, Malaysia)
Bayu Erfianto (Telkom University, Indonesia)
Track Chair
Seno Adi Putra, SSi MT (Telkom University, Indonesia) Putu Harry Gunawan (Telkom University, Indonesia) Tee Connie (Multimedia University, Malaysia) Ying Han Pang (Multimedia University, Malaysia) Raden Sumiharto (Universitas Gadjah Mada, Indonesia) Mardhani Riasetiawan (Universitas Gadjah Mada, Indonesia) Ade Romadhony (Telkom University, Indonesia)
Secretariat Chair
Siti Karimah (Telkom University, Indonesia) Shih Yin Ooi (Multimedia University, Malaysia) Rita Rismala (Telkom University, Indonesia)
Publication Chair
Dawam Dwi Jatmiko Suwawi (Telkom University, Indonesia) Anditya Arifianto (Telkom University, Indonesia)
Finance Chair
Annisa Aditsania (Telkom University, Indonesia) Siew Chin Chong (Multimedia University, Malaysia) Siti Sa'adah (Telkom University, Indonesia)
Event and Logistic Chair
Fazmah Arif (Telkom University, Indonesia) Mira Sabariah (Telkom University, Indonesia) Prati Gani (Telkom University, Indonesia)
Public Relation Chair
Z. k. a. Baizal (Telkom University, Indonesia)
CFP Chair
Wikky Fawwaz Al Maki (Telkom University, Indonesia)
Tutorial and Special Session Chair
Didit Adytia (Telkom University, Indonesia)
Sponsorship Chair
Kemas Lhaksmana (Telkom University, Indonesia)
Webmaster
Rahmat Yasirandi (Telkom University, Indonesia) Yusza Reditya Murti (Telkom University, Indonesia)
Technical Session Committee
Ade Romadhony (Telkom University, Indonesia)
Agung Toto Wibowo (Telkom University - Indonesia, Indonesia) Agus Harjoko (Universitas Gadjah Mada, Indonesia)
Angelina Prima Kurniati (Telkom University, Indonesia)
Didit Adytia (School of Computing, Telkom University, Indonesia) Hilal H. Nuha (Telkom University, Indonesia)
Idham Ananta (Universitas Gadjah Mada, Indonesia) Isman Kurniawan (Telkom University, Indonesia) Kemas Wiharja (Telkom University, Indonesia)
Mardhani Riasetiawan (Universitas Gadjah Mada, Indonesia) Niken Cahyani (Telkom University, Indonesia)
Ong Thian Song (Multimedia University, Malaysia) Parman Sukarno (Telkom University, Indonesia) Putu Harry Gunawan (Telkom University, Indonesia) Raden Sumiharto (Universitas Gadjah Mada, Indonesia) Rendi Yusuf Azhari (Universitas Gadjah Mada, Indonesia)
Risnandar Risnandar (Research Center for Informatics, Indonesian Institute of Sciences, Indonesia)
Shih Yin Ooi (Multimedia University, Malaysia) Siew Chin Chong (Multimedia University, Malaysia)
Tee Connie (Multimedia University, Malaysia)
Wahyono Wahyono (Universitas Gadjah Mada, Indonesia) Wikky Fawwaz Al Maki (Telkom University, Indonesia) Ying Han Pang (Multimedia University, Malaysia) Yunita Sari (Universitas Gadjah Mada, Indonesia)
2020 8th International Conference on Information and Communication Technology (ICoICT)
Organized byTelkom University,Multimedia University&Gajah Mada University
Prepared byEDAS Conference Services.
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Document details
Chord Recognition using FFT Based Segment Averaging and Subsampling Feature Extraction
(Conference Paper)
Sanata Dharma University, Electrical Engineering Study Program, Yogyakarta, Indonesia
Abstract
This paper proposes a feature extraction subsystem for a chord recognition system, which gives a fewer number of feature extraction coefficients than the previous ones. The method of the proposed feature extraction is FFT (Fast Fourier Transform) based segment averaging and subsampling. Guitar chords were used in developing the proposed feature extraction. In general, the method of the proposed feature extraction is as follows. Firstly, the input signal is transformed using FFT. Secondly, the left half portion of the transformed signal is processed in succession using SHPS (Simplified Harmonic Product Spectrum), logarithmic scaling, segment averaging, and subsampling. The output of subsampling is the result of the proposed feature extraction. Based on the test results, the proposed feature extraction was quite efficient for use in a chord recognition system. For the recognition rate category above 98%, the chord recognition system only required a number of seven feature extraction coefficients. In addition, for the recognition rate category above 90%, the chord recognition system only required a number of six feature extraction coefficients. © 2020 IEEE.
Author keywords
chord recognition feature extraction FFT segment averaging subsampling
Indexed keywords Engineering controlled terms:
Extraction Fast Fourier transforms
Engineering uncontrolled terms
Chord recognition Extraction coefficients FFT (fast Fourier transform) Logarithmic scaling Product spectrums Engineering main heading: Feature extraction Funding details 1
This work has been supported by The Institute of Research and Community Services of Sanata Dharma University, Yogyakarta.
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2020 8th International Conference on Information and Communication Technology, ICoICT 2020 June 2020, Article number 9166355
8th International Conference on Information and Communication Technology, ICoICT 2020; Yogyakarta; Indonesia; 24 June 2020 through 26 June 2020; Category numberCFP20ICZ-ART; Code 162426
Sumarno, L.
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© Copyright 2020 Elsevier B.V., All rights reserved. ISBN: 978-172816142-6
Source Type: Conference Proceeding Original language: English
DOI: 10.1109/ICoICT49345.2020.9166355 Document Type: Conference Paper
Sponsors: IEEE Indonesia Section,IEEE Signal Processing Society Indonesia Chapter
Publisher: Institute of Electrical and Electronics Engineers Inc.
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Chord Recognition using FFT Based Segment
Averaging and Subsampling Feature Extraction
Linggo Sumarno
Electrical Engineering Study Program Sanata Dharma University
Yogyakarta, Indonesia [email protected]
Abstract—This paper proposes a feature extraction subsystem for a chord recognition system, which gives a fewer number of feature extraction coefficients than the previous ones. The method of the proposed feature extraction is FFT (Fast Fourier Transform) based segment averaging and subsampling. Guitar chords were used in developing the proposed feature extraction. In general, the method of the proposed feature extraction is as follows. Firstly, the input signal is transformed using FFT. Secondly, the left half portion of the transformed signal is processed in succession using SHPS (Simplified Harmonic Product Spectrum), logarithmic scaling, segment averaging, and subsampling. The output of subsampling is the result of the proposed feature extraction. Based on the test results, the proposed feature extraction was quite efficient for use in a chord recognition system. For the recognition rate category above 98%, the chord recognition system only required a number of seven feature extraction coefficients. In addition, for the recognition rate category above 90%, the chord recognition system only required a number of six feature extraction coefficients.
Keywords—chord recognition, FFT, feature extraction, segment averaging, subsampling
I. INTRODUCTION
A chord will be generated if two or more notes are played at the same time. The naming of a chord is based on the origin of the notes being played. For example, if the origin of the notes are the first, third, and fifth notes on a major scale, then the chord is included in the major chord. There are many kinds of chord variations. This variation depends on the scale and the order of the notes on the scale [1].
Nowadays computers can be developed to be able to recognize the chords that are being played. Based on the previous works on chord recognition, the topic of chord recognition, which is using chroma features, is a popular topic. PCP (Pitch Class Profile) is a feature extraction that uses chroma features. PCP was introduced by Fujishima [2]. PCP produced a number of 12 feature extraction coefficients. Each feature extraction coefficient from PCP represents the power of the fundamental frequencies that exist in a chord. The original PCP from Fujishima, is still popular today. Recent studies still use the original PCP [3] [4].
In addition to the original PCP from Fujishima above, there were also a number of derivatives from the PCP. A number of PCP derivatives include, PCP that used statistical features [5], PCP that used logarithmic compression [6], Enhanced PCP [7], Improved Chromagram [8], CRP (Chroma DCT-Reduced log Pitch) Enhanced PCP [9] , and
Improved PCP [10]. A number of PCP derivatives also produced a number of 12 feature extraction coefficients.
Based on a previous work also, there was a topic of a chord recognition whose feature extraction did not use chroma features. Sumarno [11] introduced a chord recognition whose feature extraction used FFT (Fast Fourier Transform)-based segment averaging. This kind of feature extraction gave a number eight feature extraction coefficients. If we look at the number of feature extraction coefficients, we can see that the research of chord recognition for reducing the number of feature extraction coefficients is still open.
This work developed further a chord recognition whose feature extraction used the above the FFT-based segment averaging [11], by adding a process called the subsampling process. The aim of adding this process was to give even a smaller number of the feature extraction coefficients, which was less than eight coefficients. As a note, this work used the guitar chords.
II. RESEARCH METHODOLOGY
A. Development of the chord recognition system and the feature extraction subsystem
The first step in the research methodology was the development of a chord recognition system, which was focused on the feature extraction subsystem. The block
Fig. 1. The block diagram of the developed chord recognition system and its feature extraction subsystem.
WK,QWHUQDWLRQDO&RQIHUHQFHRQ,QIRUPDWLRQDQG&RPPXQLFDWLRQ7HFKQRORJ\,&R,&7
diagram of the chord recognition system and its feature extraction subsystem are shown in Fig. 1. As shown in Fig. 1, the input system is a chord signal. This signal is an isolated chord signal recorded in wav format. The system output is a text, which indicates a recognized chord signal.
As a first note, if we look at each process in the feature extraction subsystem, it is the common process. However if we look at as a whole, there is a novelty in the feature extraction subsystem compares with the previous one [11]. As a second note, the implementation of the chord recognition system was carried out using Python software. In a more detail, the input and the function of each block in Fig. 1 are described as follow.
1) Input: The input of the chord recognition system is a chord signal. This chord signal came from the Yamaha CPX 500-II acoustic-electric guitar, as shown in Fig. 2. This chord signal is an isolated chord signal, which recorded in wav format. There are seven chord signals from the major chords of C, D, E, F, G, A, and B [11]. Chord signals recording was carried out using a sampling frequency that met Shannon's sampling theorem [12]:
fs 2 fmax (1)
where fs is the sampling frequency, and the fmax is the
highest frequency component of the chord signal to be sampled. This study used a sampling frequency of 5000 Hz. According to the Shannon’s sampling theorem above, the sampling frequency had exceeded the highest frequency component of 392 Hz (G4 tone) of the G chord [11].
The duration of the chord signals recording was two seconds. Based on the results of visual evaluations of the amplitude of the chord signal, the choice of two seconds duration was enough to get more than half of the chord signal that already in a steady state condition. As a note, the accurate chord information was available in this steady state condition.
2) Normalization: Normalization is a process for equalizing the maximum value of the sequence of signal data input. In this case, the maximum value is -1 or 1. Normalization is formulated as below.
xout = xin / max (|xin|) (2)
where xin and xout are the input and output signal data
sequences of the normalization process, respectively. The normalization process is needed because the input signal data sequences have different maximum values.
3) Silence and transition cutting: Silence and transition cutting is a process for removing the silence and transition region of a signal data sequence. This region is on the left side of a signal data sequence. Silence and transition cutting is carried out as follows. Firstly, based on the visual observations, in order to remove the silence region, this
work needed the data threshold |0.5|. Starting from the leftmost data of the signal data sequence, if the data was less than |0.5| then the data was removed. Secondly, based on the visual observations also, in order to remove the transition region, this work needed a duration of 200 milliseconds at the leftmost region of the signal data sequence to be removed [11].
4) Frame blocking: Frame blocking is a process for acquiring a short signal data sequence, which called a signal frame, from a long signal data sequence [13]. Frame blocking is carried out by acquiring a signal frame at the leftmost region of the long signal data sequence. The purpose of using the frame blocking process is to reduce the number of signal data to be processed further. The effect of reducing the number of signal data is the reduction in computational time needed for signal data processing. This work used 256 points of blocking frame length [11].
5) Windowing: In the time domain, windowing is a process for decreasing the discontinuities that appear at the left and the right edges of the signal data sequence [13]. In the frequency domain, this reduction will eliminate the emergence of spectral leakage at the output the FFT process. This work used Hamming window [14] for windowing process. This kind of window has been widely used in the digital signal processing field [15]. The width of the window was the same as the frame blocking length.
6) FFT: FFT is a process for transforming a signal data sequence from the time domain to the frequency domain. This work used FFT radix-2. This kind of FFT has been widely used in the digital signal processing field [15]. The length of the FFT was the same as the frame blocking length. In addition, there were additional calculations of absolute values for the FFT results. This was necessary because the subsequence process, namely SHPS, required positive values.
7) Symmetry cutting: Symmetry cutting is a process for removing the right half portion of the FFT result. As a note, the left and the right half portion of the FFT result show a symmetry property. Therefore, it was sufficient if this work used only the left portion of the FFT result.
8) SHPS: SHPS (Simplified Harmonic Product Spectrum) is a process for reducing the harmonic signal data. The reduction of these harmonic signal data, visually, will show a clearer difference, between a signal data sequence and the other signal data sequences [11]. This work used the SHPS that was introduced by Sumarno [11]. This SHPS is a derivative of HPS (Harmonic Product Spectrum) that was introduced by Noll [16].
9) Logarithmic scaling: Logarithmic scaling is a process for increasing the number of significant local peaks. Based on the previous research [11], in the chord recognition that using segment averaging, if we increase the number of significant local peaks, it can increase the recognition rate. Logarithmic scaling is formulated as below.
Xout= log (Xout + 1) (3)
where Xin and Xout are respectively the input and the output
signal data sequences, from the logarithmic scaling process. The value is the logarithmic scale factor. Adding the value '1' to the logarithmic scale factor formula is to avoid the zero
Fig. 2. The guitar for this work.
value logarithm, which will give an infinite value. This work used a logarithmic scale factor of 50 [11].
10) Segment averaging: Segment averaging is a process for obtaining a short signal data sequence from a long signal data sequence. The segment averaging was initially inspired from Setiawan [17]. Next, the segment averaging was developed by Sumarno [18]. Algorithmically, the segment averaging process is shown below.
1. Suppose Yin is an input signal data sequence in the
segment averaging process. Yin has positive values
and has a length N, with N = 2q for q 0 which is a
positive integer.
2. Set the segment length L points, with L = 2p for 0 p
q which is also a positive integer.
3. Cut Yin uniformly using the segment length L points.
This cutting will give a number of M segments namely S1, S2, …, SMwith
M = N / L (4) As a note, each segment has a data sequence with the length L points.
4. Calculate the average value of the data sequence in each segment, and then arrange it into the following
Yout data sequence.
Yout = { avg(S1), avg(S2), …, avg(SM)}
The Yout data sequence is the output signal data sequence
in the segment averaging process. This Yout data sequence
has the length M points. Based on equation (4), the output of the segment averaging has a length of 2n points, with n = q -
p. This work used the segment averaging output length 4, 8, 16, and 32 points.
11) Subsampling: Subsampling is an advanced process of the segment averaging process above, in order to get the signal data sequences that are even shorter. In this work, the output of subsampling process is also called the result of the feature extraction data. The effect of obtaining the shorter feature extraction data is the reduction in storage that needed to store a number of the feature extraction data. Interpolation is a way to do the subsampling process. An interpolation method that has been widely used is spline interpolation [19]. In this work evaluated linear, quadratic, and cubic spline interpolation methods [20]. In addition, this work also evaluated the subsampling output of 1-8 coefficients.
12) Chord database: Chord database is a collection of a number of chords reference feature extraction (C, D, E, F, G, A, and B). The development of the chord database was carried out as follows. The first one was recording a number of 10 training chord samples for each chord (C, D, E, F, G, A, and B). It was assumed that by using a number of 10 training chord samples, all variations of each chord signal from a guitar musical instrument have been obtained. The second one was processing the feature extraction of all the training chord samples by carrying out the normalization process up to subsampling process that are shown in Fig. 1. For each chord (C, D, E, F, G, A, and B), the feature extraction of 10 training chord samples will produce 10
feature extraction data sequence. The third one was carrying out the averaging calculation for each chord as follows.
= = 10 1 , 10 1 i Ti T Y Z (5)
where T is a chord (C, D, E, F, G, A, or B), {YT,i | 1 i
10} are 10 feature extraction of a T chord, and ZT is a
reference feature extraction from the average of 10 feature extractions of a T chord. The last one was collecting a number of seven reference feature extractions namely ZC,
ZD, ZE, ZF, ZG, ZA, and ZB in the chord database.
13) Similarity calculation: Similarity calculation is a process for calculating the similarity values between a chord feature extraction of an input signal, and a number of chords reference feature extraction (C, D, E, F, G, A, and B) stored in a chord database. Thus, at the output of the similarity calculation process, there are a number of seven similarity values. This work used cosine similarity. This kind of similarity has been popularly used [21].
14) Chord decision: Chord decision is a process for determining the output text (C, D, E, F, G, A, or B), which indicates a chord that is recognized. The chord decision process was carried out as follows. The first one was finding the largest similarity value out of a total of seven similarity values, which are the outputs of the similarity calculation process. The second one was determining a recognized chord. A chord (C, D, E, F, G, A, or B) associated with the largest similarity value is determined as a recognized chord. As a note, an output process that is determined based on the largest similarity value indicating that it uses the template matching method [22] [23].
B. Developing Test Chords
The second step in the research methodology is the developing of the test chords, which were used for testing the chord recognition system. This work recorded 20 test chord samples for each chord (C, D, E, F, G, A, and B). Therefore, there were a number of 140 test chord samples.
C. Testing and Recognition Rate Calculation
The final step in the research methodology is testing and calculating the recognition rate. Testing was carried out using 140 test chord samples, for the segment averaging outputs 4, 8, 16, and 32 points, the number of feature extraction coefficients 1-8, and the methods of subsampling namely linear, quadratic, and cubic spline interpolation. The recognition rate calculation is the calculation of the ratio (expressed in percent), between the number of correctly recognized chords, and a number of 140 test chord samples.
III. RESULTS AND DISCUSSIONS A. Results
The chord recognition system shown in Fig. 1 has been tested for the chords of a guitar shown in Fig. 2. This test was carried out for the segment averaging outputs 4, 8, 16, and 32 points, the number of feature extraction coefficients 1-8, and the methods of subsampling namely linear, quadratic, and cubic spline interpolation. The results are shown in TABLE I. As a note, the number of feature extraction coefficients are correspond with the subsampling outputs.
