JP2008257084A - Hlac feature quantity extracting method and feature quantity extracting device, by binarizing one-dimensional signal - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an HLC (high dimensional local autocorrelation) feature quantity extracting method and a feature quantity extracting device, by binarizing one-dimensional signal, based on binary HLAC which is suitable for hardware, with which high speed processing can be performed. <P>SOLUTION: The one-dimensional signal is binarized by pulse width modulation (PWM), and the HLAC feature quantity is calculated by applying one dimensional binary HLAC to the one-dimensional binary signal. Each amplitude value of the one-dimensional signal is binarized by any one of linear quantization, μ-Law quantization or gray code quantization, and two-dimensional binary HLAC is applied to a binary image which is generated as its time sequence. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  In the present invention, processing such as abnormality detection, recognition of specific signals, search, or counting is performed from various one-dimensional signals including higher frequencies in addition to acoustic signals such as voice, music, and environmental sounds, or electrocardiogram waveforms and earthquake waveforms. The present invention relates to an effective HLAC feature quantity extraction method and feature quantity extraction apparatus.

Techniques for extracting features from a moving image using a high-order local autocorrelation function (HLAC) and detecting abnormal motion and tracking a moving object in real time have already been proposed in the following Patent Documents 1 to 3.
Japanese Patent Laying-Open No. 2005-092346 JP 2006-079272 A JP 2006-163452 A

In Patent Documents 1 to 3, feature extraction is performed from an image (two-dimensional) or a moving image (three-dimensional) using high-order local autocorrelation (HLAC).
On the other hand, a technique for extracting HLAC features from a one-dimensional signal such as an acoustic signal has not yet been established.
In the conventional extraction of HLAC features from (moving) images, a binarized image is used, so that higher-order correlation (that is, whatever the luminance value of a pixel is raised) does not cause an overflow and can be calculated stably. . However, in the case of a one-dimensional signal, for example, if one sample value is quantized with 16 bits, it has a dynamic range of about −30,000 to +30,000, and a high-order correlation is performed so as not to cause overflow from the value. In order to obtain it, the load of the hardware or software of the arithmetic device increases. In this case, there is a problem that real-time processing becomes difficult in HLAC feature extraction from an ultrasonic band signal or a one-dimensional signal including higher frequency components. In addition, since the digit of the value is greatly different between the high-order correlation and the low-order correlation, for example, when performing principal component analysis using the feature amount, it is difficult to perform stable numerical calculation.

  In view of the above problems, the present invention provides a HLAC feature quantity extraction method and a feature quantity extraction apparatus based on binary one-dimensional signal based on binary HLAC that is capable of high-speed processing and suitable for hardware. With the goal.

Hereinafter, a signal means a one-dimensional signal unless otherwise specified.
In order to extract a feature value from an analog signal using a one-dimensional binary HLAC, it is necessary to binarize the amplitude value of the analog signal. In the present invention, “(1) Method of using pulse width modulation” and “(2) Gray code or the like converted to binary notation are arranged in time series for binarization of amplitude, and two-dimensional binary is obtained. Two types of methods “method of generating image” are used.
(1) Method using pulse width modulation (PWM):
PWM is used as one of means for binarizing the analog input signal, and a one-dimensional binary HLAC feature is extracted from the binarized signal.
The PWM signal is a modulation method that increases or decreases the pulse width in proportion to the amplitude value of the analog input signal. The amplitude value of the PWM signal is a positive and negative binary signal. A circuit diagram of hardware for realizing this modulation function is shown in FIG.

FIG. 1 is a circuit diagram of a one-dimensional binary HLAC feature amount calculation circuit according to the present invention. 1 includes a comparator 2, a sampling means 3, a register 4, a counter 5, a connection matrix circuit 9 composed of an output line of a shift register and an input line of an AND circuit, a logic It consists of an AND circuit 6, a cumulative addition circuit 7, and a latch circuit 8 constituting a product circuit.
The input observation signal and the reference triangular wave signal are compared by the comparator 2, the PWM output signal of the comparator 2 is sampled by the sampling means 3 using an arbitrary sampling frequency as a reference clock, and sequentially stored in the register 4. As for the storage state of the register 4, the output of the AND circuit 6 reflecting the connection state of the matrix circuit 9 is added by the cumulative addition circuit 7, and this addition value is taken out from the latch circuit 8 as “PWM + BinHLAC signal”. The connection matrix circuit 9 defines a mask used for calculation of HLAC.
In this one-dimensional binary HLAC feature quantity calculation circuit 1, since the PWM signal is obtained as a comparison output by comparing the reference triangular wave signal with the analog input signal that has passed through the band limiting filter, it is realized as hardware. Is easy. Note that the frequency of the triangular wave needs to be set sufficiently higher than the maximum frequency of the analog input signal.

FIG. 2 is a diagram illustrating a process of binarizing an analog input signal by PWM processing.
FIG. 2A is a waveform diagram of an analog input signal. FIG. 2B is a waveform diagram of a pulse signal obtained by performing PWM processing on the analog input signal of FIG. FIG. 2C is a waveform diagram of a signal obtained by obtaining a difference (details will be described later) of the PWM signal in FIG. 2B in order to further emphasize the change in the PWM signal.
The PWM signal output from the comparator 2 in FIG. 1 is a continuous time binary signal. The binary signal is sampled by the sampling means 3 at a certain sampling frequency, and each sample value is output with 1 bit. The 1-bit sample value is stored in the shift register 4 that shifts in synchronization with the sampling frequency. Since the shift register 4 shown in FIG. 1 shifts bits from top to bottom, the uppermost bit represents the sample value at the current time, and the lowermost bit represents the past sample value.

ｆ（ｍ）は離散時間信号、Ｎは次数、Ｍはフレームのサンプル数、（ａ０，ａ１，ａ２，…，ａＮ）はマスクパターンを表す。但し、ａ０＝０である。マスクパターンは、シフトレジスタのレジスタ幅Ｗ（記憶するビット数）と相関の次数Ｎで一意に決まる。 When the difference signal of the PWM signal shown in FIG. 2C is obtained, it is obtained by obtaining an exclusive logical sum between adjacent bits of the shift register 4.
Hereinafter, a method for calculating a one-dimensional binary HLAC feature amount from a PWM signal or a difference signal thereof will be described.
The one-dimensional binary HLAC of the discrete time signal is obtained by the following equation.

f (m) is a discrete-time signal, N is the order, M is the number of sampled frames, and (a0, a1, a2,..., aN) are mask patterns. However, a0 = 0. The mask pattern is uniquely determined by the register width W (number of bits to be stored) of the shift register and the correlation order N.

FIG. 3 shows a mask pattern of the register width W = 4 of the present invention.
The blacked out position indicates the bit used for calculating the correlation. The leftmost of each mask pattern indicates a0, and the bits a1, a2,. For example, the primary mask pattern has three mask patterns in which a0 is always fixed to 0 and a1 changes to 1, 2, and 3.
In the circuit diagram of FIG. 1, the mask pattern is mounted by operating the connection of the connection matrix circuit 9 constituted by the output line of the shift register and the input line of the AND circuit. As the dedicated hardware, for example, an element such as a field programmable gate array (FPGA) is used, and this connection can be dynamically changed, so that a mask pattern of an arbitrary order can be mounted. The connection shown in FIG. 1 shows a mask pattern when the register width shown in FIG.

The correlation value of the mask pattern corresponding to the output of each logical product circuit is output, and the output is input to the cumulative addition circuit 7 and cumulatively added in synchronization with the clock signal.
The Clock signal is also input to the counter circuit, and the number of additions by the cumulative addition circuit 7 is counted. When the number of additions matches the number of samples in the frame, a control signal is output from the counter circuit 5 and the output value of each cumulative addition circuit 7 at that time is stored in the latch circuit 8. Thereafter, the value of the cumulative addition circuit 7 is returned to zero, and the feature amount calculation for the next frame is initialized. The control signal from the counter circuit 5 is also used as an interrupt signal for notifying an external circuit that the HLAC feature value of one frame has been determined.

(2) A method of generating a two-dimensional binary image by arranging time-series amplitude values converted into binary notation using a Gray code or the like:
FIG. 4 is a diagram showing a procedure for calculating a feature amount by a method of generating a two-dimensional binary image by arranging amplitude values converted into binary notation using the Gray code of the present invention in time series.
The flowchart of FIG. 4 will be described. In the figure, S means an abbreviation of step.

Start (1) Capture one-dimensional analog input signal (S1):
A one-dimensional analog input signal as an observation signal is subjected to analog-digital conversion (A / D conversion).
(2) Sample value conversion (μ-Law, GrayCode, etc.) (S2):
The quantized value of the digital signal converted in S1 is used in addition to typical linear quantization means, μ-Law quantization means or Gray code quantization means widely used for audio signal compression, etc. To convert.
(3) Generation of binary image (S3):
Binary image data is generated from the time-series data of the discrete time quantized sample values obtained in S2. At this time, for example, when each sample value is quantized with 8 bits and the 8-bit bit patterns are arranged in chronological order, a binary image is generated as shown in FIG.

FIG. 5 is an explanatory diagram of a method for calculating a feature by applying a two-dimensional (2D) binary HLAC to a binary image of the present invention. The sample value of the function X (t) at time t (the value of the black circle on the analog signal) is expressed as a binary value as indicated by an arrow to generate a binary image pattern.
(4) Calculation of two-dimensional binary HLAC (S4):
In this way, the two-dimensional binary HLAC process is applied to the binary image data to calculate the HLAC feature quantity.
(5) HLAC feature output (S5):
The HLAC feature value calculated in S4 is stored in the storage means, and is read for processing such as abnormality detection, specific signal recognition, search, or counting.
Finish

Specific means for solving the problem are as follows.
(1) The HLAC feature quantity extraction method is characterized in that a one-dimensional signal is binarized by PWM, and a one-dimensional binary HLAC is applied to the one-dimensional binary signal to obtain an HLAC feature quantity.
(2) In the HLAC feature quantity extraction method, each amplitude value of the one-dimensional signal is binarized by any one of linear quantization, μ-Law quantization, and Gray code quantization, and the time series thereof A two-dimensional binary HLAC is applied to a binary image generated as follows.
(3) A HLAC feature quantity extraction circuit for obtaining a HLAC feature quantity by binarizing a one-dimensional signal by PWM and applying the one-dimensional binary HLAC to the one-dimensional binary signal applies the one-dimensional binary HLAC. Sometimes, the mask pattern used for calculation of HLAC can be changed to an arbitrary pattern shape.

HLAC feature values that have been used for feature extraction from two-dimensional images and three-dimensional moving images can be applied to various one-dimensional signals such as speech / acoustic signals, ECG waveform signals, and seismic waveform signals. Become.
High-speed feature extraction processing can be performed by binarizing the one-dimensional analog input signal. In particular, in the feature extraction method combining PWM and one-dimensional binary HLAC, high-speed processing is possible by configuring hardware using the circuit of the present invention, and the mask pattern can be dynamically changed. A device is realized.
As another binarization method, the analog input signal is converted into a digital signal, and the 2D / binary HLAC is applied to the binary image generated using the Gray code, thereby dramatically improving the stability of feature extraction. Improve.

  Embodiments of the present invention will be described in detail with reference to the drawings.

A description will be given for each case according to each method of binarizing the one-dimensional analog input signal.
(1) Method using Pulse Width Modulation (PWM):
Ten types of voice data are generated by randomly connecting words uttered by the same male speaker. The number of words included in the generated audio data is counted using the HLAC feature value extracted from each of the PWM signal of the audio signal and its differential signal. The number of words used in the experiment is increased from 60 to 100 in increments of 10, and the counting accuracy using the proposed feature amount is examined.
An audio signal with a sampling frequency of 16 kHz used in the experiment was up-sampled 10 times, and compared with a triangular wave with a basic frequency of 16 kHz, a PWM signal for binarizing the analog signal was generated.
In the calculation of the one-dimensional binary HLAC, the number of mask points was 8, the mask point interval was 100 samples, and the maximum order was 5.

Table 1 shows the number of correct answers in which all the numbers of words were correctly counted from 10 types of speech data generated using 60 to 100 words.

Table 1. Number of correct answers for each method

(2) A method of generating a two-dimensional binary image by arranging time-series amplitude values converted into binary notation using a Gray code or the like:
100 voice data are generated by randomly connecting words uttered by the same male speaker. How many words are included in the generated voice data,
(A) means for applying 1D-shading HLAC to a one-dimensional signal with normalized amplitude;
(B) means for applying a 1D-2 value HLAC to the PWM signal;
(C) Means for converting a sample value time series of a one-dimensional signal into a two-image pattern by linear quantization and applying a 2D-2 value HLAC. (D) A binary image pattern of a sample value time series of a one-dimensional signal by GrayCode. Means for converting to and applying a 2D-2 value HLAC;
The word counting experiment is performed with these four types.

The number of words used in each experiment is matched with the number of dimensions of each HLAC feature. The number of dimensions of each HLAC feature amount is as follows.
1.1D-light HLAC;
Number of mask points = 6, maximum order = 2 (features are 28 dimensions)
2. PWM + 1D-2 value HLAC;
Number of mask points = 6, maximum order = 3 (features are 26 dimensions)
3. Linear quantization + 2D-2 value HLAC;
Mask = 3 × 3, maximum order = 2 (features are 25 dimensions)
4). Gray code + 2D, binary HLAC;
Mask = 3 × 3, maximum order = 2 (feature amount is 25 dimensions).

In principle, it is impossible to distinguish more words than the number of dimensions of the feature quantity. If the features of different word sounds can be appropriately expressed as having different feature quantities, it is possible to distinguish the same number of word sounds as the number of dimensions. If the feature amount is not appropriate, the feature amounts obtained from different word sounds have a linear dependency relationship, and the same number of word sounds as the number of dimensions cannot be distinguished.
By changing the mask point interval of the mask pattern, the performance of the feature amount changes greatly. The mask point interval of the optimum mask pattern basically depends on the signal to be analyzed. Regarding the methods 1 and 2, the experiment is performed while changing the mask point interval from 0.0625 ms to 25 ms. Regarding the methods 3 and 4, the experiment is performed while changing the mask point interval along the bit pattern axis from 1 to 7 bits wide and the mask point interval along the time axis from 0.0625 ms to 25 ms.

The performance of the feature quantity is evaluated based on whether or not the simultaneous counting of the same number of word sounds as the number of dimensions is possible and how stably the word sounds can be simultaneously measured regardless of the mask point interval.
The number of word sounds is simultaneously counted from the generated 100 samples, and the counting is successful only when the counting results of all the samples are correct. If even one of the 100 samples has an incorrect count result, the count fails.
For each feature amount, a counting experiment is performed while changing the mask point interval, and a counting success appearance ratio is obtained as a ratio of the counting success count to the total number of experiments. A larger count success appearance ratio indicates that the word count can be stably performed without depending on the mask point interval, which means that the performance as a feature amount is high.

(Experimental result)
FIG. 6 is a diagram showing the count success appearance rate of each method.
As shown in FIG. 6, the coefficient success appearance rate [%] is 47.37 in the method of “1D · Gray (shading) HLAC”, 48.68 in the method of “PWM + 1D · Bin (binary) HLAC”, The method of “Linear (linear quantization) + 2D · Bin (binary) HLAC” is 0.19, and the method of “GrayCode (gray code) + 2D · Bin (binary) HLAC” is 57.89.
In all the methods shown in FIG. 6, since the successful count appearance rate is not zero, it can be understood that the number of word sounds can be correctly counted if the mask point interval is set appropriately.
It can be said that the “PWM + 1D · Bin (binary) HLAC” method has higher performance as a feature amount than the “1D · Gray (light / dark) HLAC” method. With regard to the method of “Linear (linear quantization) + 2D · Bin (binary) HLAC”, the performance largely depends on the mask point interval, so it is necessary to accurately check the characteristics of the signal to be analyzed in advance. On the other hand, in the method of “GrayCode (Gray code) + 2D · Bin (binary) HLAC”, the dependence on the mask point interval is reduced, and the performance as the feature amount is greatly improved.

It is a circuit diagram of the one-dimensional binary HLAC feature quantity calculation circuit of the present invention. It is a figure which shows the process progress which binarizes an analog input signal by PWM process. The mask pattern of the register width W = 4 of this invention is shown. It is a figure which shows the procedure which calculates a feature-value by the method of arranging the amplitude value converted into the binary notation using the Gray code of this invention in time series, and producing | generating a two-dimensional binary image. It is explanatory drawing of the method of calculating a characteristic by applying a two-dimensional binary HLAC with respect to the binary image of this invention. It is a figure which shows the count success appearance rate of each method.

Explanation of symbols

1 .. One-dimensional binary HLAC feature quantity calculation circuit 2 .. Comparator 3 .. Sampling means 4. Shift register 5. Counter 6.
7 .. Cumulative addition circuit 8 .. Latch circuit 9 .. Matrix circuit

Claims (3)

An HLAC feature quantity extraction method characterized in that a one-dimensional signal is binarized by PWM, and an HLAC feature quantity is obtained by applying the one-dimensional binary HLAC to the one-dimensional binary signal. Each amplitude value of the one-dimensional signal is binarized by any one of linear quantization, μ-Law quantization, and Gray code quantization, and two-dimensionally generated into a binary image generated as a time series thereof. 2. The HLAC feature quantity extraction method according to claim 1, wherein binary HLAC is applied. In an HLAC feature quantity extraction circuit that binarizes a one-dimensional signal by PWM and applies a one-dimensional binary HLAC to the one-dimensional binary signal to obtain an HLAC feature quantity,
An HLAC feature quantity extraction circuit characterized in that when applying the one-dimensional binary HLAC, a mask pattern used for calculation of HLAC can be changed to an arbitrary pattern shape.

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