CN113758713B - Adaptive recognition method for rough audio frequency band - Google Patents
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Abstract
The invention discloses a rough audio frequency band self-adaptive identification method, which comprises the following steps: s1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively carrying out band-pass filtering on the noise signals to obtain sound signal matrixes BP of different critical frequency bands; s2, dividing BP respectively according to self-defined rotation speed increment to obtain sound signal matrixes T of different rotation speed ranges n The method comprises the steps of carrying out a first treatment on the surface of the S3, respectively aiming at the sound signal matrix T n Performing Hilbert transformation, and calculating to obtain corresponding envelope matrix E n The method comprises the steps of carrying out a first treatment on the surface of the S4, respectively matching the envelope line matrix E n Performing Fourier transformation, and calculating to obtain modulation depth matrix D under different modulation frequencies n The method comprises the steps of carrying out a first treatment on the surface of the S5, determining a modulation depth matrix O of the 0.5-order modulation order at different rotating speeds based on a peak value holding principle according to the corresponding relation between the modulation frequency and the modulation order n The method comprises the steps of carrying out a first treatment on the surface of the S6, drawing a time or rotating speed-critical frequency band cloud chart of 0.5-order modulation depth, and identifying the characteristic frequency band of rough sound of the automobile or the engine. The invention can rapidly identify the characteristic frequency band of the rough sound of the automobile or the engine.
Description
Technical Field
The invention belongs to the technical field of NVH (noise vibration and harshness) of automobiles, and particularly relates to a rough audio frequency band self-adaptive identification method.
Background
Reciprocating engines are typically rotary machines, with operational noise having significant order modulation characteristics. Related studies have shown that rough acoustic complaints of automobiles or engines are mainly related to the intensity of 0.5 order modulation of engines, and modulation of appropriate frequency bands can increase the sound motion feeling, giving a pleasant riding experience, but if the 0.5 order modulation phenomenon is too prominent or appears in an inappropriate frequency band, subjective high annoyance feeling may be caused. According to the analysis experience of the past engineering cases, the modulation phenomenon is generally distributed in a discrete manner in a plurality of frequency bands of noise signals of automobiles or engines, but the modulation depth is obviously different to different degrees. Therefore, how to quickly identify the characteristic frequency band of the rough sound, and further form strong correlation with the subjective sound quality complaints of the human ears, is a key for analyzing the generation mechanism of the rough sound, and is also an important reference basis for a subsequent optimization scheme.
Patent document CN112326267a discloses a method and a system for determining an acceleration coarse acoustic effect result, which determine an initial noise frequency corresponding to a broadband resonance frequency by accelerating a noise cloud chart, and then jointly determine a coarse acoustic frequency band by combining filtering playback and vibration frequency of a suspended passive end. The method has the defects that a plurality of vibration and noise measuring points are required to be synchronously arranged, the testing and subsequent analysis processes are complicated, and the coarse audio frequency band is required to be subjected to multi-layer subjective screening when being confirmed, so that the uncertainty is large and the self-adaptability is poor. In summary, the existing analysis methods for the rough sound of the automobile or the engine have few and obvious defects, and cannot effectively guide the rapid identification of the rough sound frequency band.
Therefore, it is necessary to develop a method for adaptively recognizing a coarse audio band.
Disclosure of Invention
The invention aims to provide a rough sound frequency band self-adaptive identification method, which can quickly identify the characteristic frequency band of rough sound of an automobile or an engine by drawing a time (or rotating speed) -critical frequency band cloud chart of 0.5-order modulation depth.
The invention discloses a rough audio frequency band self-adaptive identification method, which comprises the following steps:
step 1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively carrying out band-pass filtering on the noise signals according to a critical frequency band division principle to obtain sound signal matrixes BP of different critical frequency bands;
Step 3, respectively to the sound signal matrix T n Performing Hilbert transformation, and calculating to obtain corresponding envelope matrix E n ；
Step 5, according to the corresponding relation between the modulation frequency and the modulation order, determining a modulation depth matrix O of the 0.5-order modulation order under different rotating speeds based on a peak value maintaining principle n ；
And 6, drawing a time or rotating speed-critical frequency band cloud chart of 0.5-order modulation depth, and identifying the characteristic frequency band of rough sound of the automobile or the engine through transverse comparison.
Optionally, the step 1 specifically includes:
the engine rotating speed signal and the automobile or engine noise signal are acquired by adopting the same time sampling rate, the noise signals are respectively subjected to band-pass filtering, and the relation between the center frequency and the bandwidth is determined by the following formula:
BW n ＝(25+75×(1+1.4×(f c /1000) 2 ) 0.69 )×ΔBark；
wherein BW is n For critical band bandwidth, n is the number of critical bands, Δbark is the critical band delta, f c Is the critical band center frequency;
and obtaining sound signal matrixes BP of different critical frequency bands.
Optionally, the step 2 specifically includes:
according to the beginningDetermining the center point of each data block according to the initial rotation speed and the rotation speed increment, and then determining the start and stop points of each data block according to the time sampling rate and the frequency resolution to obtain sound signal matrixes T of different rotation speed ranges n 。
Optionally, the step 3 specifically includes:
for T n Hilbert transformation is carried out in a segmented mode, and absolute values are taken as an envelope matrix E n ：
E n ＝|Hilbert[T n ]|。
Optionally, the step 4 specifically includes:
first, for E n Performing FFT conversion in segments, and taking absolute values to obtain amplitude frequency spectrums:
wherein F is n For the corresponding spectrum matrix, A 0 Is an amplitude matrix corresponding to 0Hz, A i For amplitude matrix corresponding to non-zero frequency, f i Represents the analysis frequency, t represents the time,
representing phase;
then according to A 0 And A i Calculating a modulation depth matrix D n ：
Optionally, the step 5 specifically includes:
firstly, determining upper and lower limits of 0.5-order modulation frequency according to a self-defined order width;
then, based on peak hold principle in upper and lower limit frequency range, determining modulation depth matrix O of 0.5 order modulation order at different rotation speeds n 。
Optionally, the step 6 specifically includes:
and (5) comparing time or rotating speed-critical frequency band cloud pictures of different noise signals with 0.5-order modulation depths, and rapidly identifying and obtaining the characteristic frequency band of rough sound of the automobile or the engine.
The invention has the following advantages: the method has self-adaptability, adopts a unified critical frequency band division principle to carry out filtering treatment, accords with the nonlinear auditory characteristic of human ears, and avoids the uncertainty of selecting a filtering frequency band according to a frequency spectrum cloud picture. According to the invention, the 0.5-order modulation depth cloud patterns of different noise signals are transversely compared, and the characteristic frequency band of the rough sound of the automobile or the engine can be rapidly identified, so that the working flow of testing and analyzing is greatly simplified, and meanwhile, a clear guiding direction is provided for the engineering improvement scheme of the rough sound.
Drawings
FIG. 1 is a schematic flow chart of the present embodiment;
FIG. 2 is a diagram showing a comparison of a 0.5-order modulated depth cloud image of an in-car noise signal of an automobile A;
fig. 3 is a schematic diagram of a 0.5-order modulation depth cloud of a near-field noise signal of an engine B.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
In this embodiment, a method for adaptively identifying a coarse audio band includes the following steps:
(1) Synchronously collecting engine rotation speed signals and automobile or engine noise signals, respectively carrying out band-pass filtering on the noise signals according to a critical frequency band division principle to obtain sound signal matrixes BP of different critical frequency bands, wherein the sound signal matrixes BP specifically comprise:
the method comprises the steps of collecting an engine rotating speed signal and an automobile or engine noise signal by adopting the same time sampling rate, and respectively carrying out band-pass filtering on the noise signals, wherein the relation between the center frequency and the bandwidth is determined by the following formula:
BW n ＝(25+75×(1+1.4×(f c /1000) 2 ) 0.69 )×ΔBark；
wherein BW is n For critical band bandwidth, n is the number of critical bands, Δbark is the critical band delta, f c Is critical frequency band center frequencyThe rate.
The final divided sound signal matrix is BP.
In this example, the number of critical bands n=47, the critical band bandwidth Δbark=0.5, and the upper and lower limit frequencies corresponding to each critical band are shown in table 1.
TABLE 1
(2) BP is divided respectively according to the self-defined rotation speed increment to obtain sound signal matrixes T of different rotation speed ranges n The method specifically comprises the following steps:
according to the initial rotation speed R 0 And the rotation speed increment DeltaR determines the center point N (R m ) mid The start and stop points of each data block are then determined from the time sampling rate fs and the frequency resolution df:
N(R m ) 1 ＝N(R m ) mid -fs/2df；N(R m ) end ＝N(R m ) mid +fs/2df.
wherein m=1, 2, …, m R ，m R Is the total number of data blocks of the rotating speed sequence, wherein N (R m ) 1 Is the start point of the mth data block, N (R m ) end Is the dead point of the mth data block.
The finally divided sound signal matrix is T n 。
(3) Respectively to the sound signal matrix T n Performing Hilbert transformation, and calculating to obtain corresponding envelope matrix E n The method specifically comprises the following steps:
for T n Hilbert transformation is carried out in a segmented mode, and absolute values are taken as an envelope matrix E n ：
E n ＝|Hilbert[T n ]|。
(4) Respectively to envelope line matrix E n Performing Fourier transform (FFT) to obtain modulation depth matrix D under different modulation frequencies n The method specifically comprises the following steps:
first, for E n By sectioningFFT conversion is carried out, and absolute values are taken to obtain amplitude frequency spectrums:
wherein F is n For the corresponding spectrum matrix, A 0 For amplitude matrix (i.e. DC component matrix) corresponding to 0Hz, A i For amplitude matrix (i.e. alternating component matrix) corresponding to non-zero frequency, f i Represents the analysis frequency, t represents the time,
representing the phase.
Then according to A 0 And A i Calculating a modulation depth matrix D n ：
(5) According to the corresponding relation between the modulation frequency and the modulation order, determining a modulation depth matrix O of the 0.5-order modulation order under different rotating speeds based on a peak hold principle n The method specifically comprises the following steps:
first, according to the custom order width O w Determining an upper limit f of the 0.5-order modulation frequency mu Lower limit f md ：
Wherein R is the rotation speed.
Then, the modulation depth of the 0.5-order modulation order at different rotational speeds is determined based on the peak hold principle:
O n ＝max[D n ],f m ∈[f md ,f mu ]。
(6) Drawing a time (or rotating speed) -critical frequency band cloud chart of 0.5-order modulation depth, and identifying the characteristic frequency band of rough sound of an automobile or an engine through transverse comparison, wherein the method specifically comprises the following steps:
and (5) comparing time (or rotating speed) of different noise signals with a modulation depth of 0.5-order with a critical frequency band cloud picture, and identifying and obtaining the characteristic frequency band of rough sound of the automobile or the engine.
In summary, the complete algorithm flow is shown in FIG. 1.
Fig. 2 shows a cloud plot of the 0.5-order modulation depth of the noise signal near the ear of a car a over time and the critical frequency band, and the signal corresponding to fig. 2 (a) has a rough sound characteristic and the signal corresponding to fig. 2 (b) has no rough sound characteristic. The characteristic critical frequency band of the coarse sound is 3.5-4.5 barks, namely the middle-low frequency band of 300-450 Hz, which is determined by calculation and transverse comparison.
Fig. 3 shows a cloud of 0.5 order modulation depth of near-field noise signal of an engine B over time and critical frequency band, respectively, and the signal corresponding to fig. 3 (a) is subjectively evaluated to have a coarse acoustic characteristic, while the signal corresponding to fig. 3 (B) is not. The characteristic critical frequency band of the coarse sound is determined to be 10bark-17.5bark through calculation and transverse comparison, namely, the middle-high frequency band of 1170Hz-4000 Hz.
Time in fig. 2 and 3 is Time, critical band is Critical band, and Modulation Degress is modulation depth
According to the self-adaptive recognition method of the characteristic frequency band of the rough sound, provided by the invention, the working flow of testing and analyzing is greatly simplified, and meanwhile, a definite guiding direction is provided for efficiently formulating the engineering improvement scheme of the rough sound.
Claims (5)
1. The adaptive coarse audio frequency band identification method is characterized by comprising the following steps of:
step 1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively carrying out band-pass filtering on the noise signals according to a critical frequency band division principle to obtain sound signal matrixes BP of different critical frequency bands;
step 2, dividing BP according to the self-defined rotation speed increment to obtain sound signal matrixes T of different rotation speed ranges n ；
Step 3, respectively to the sound signal matrix T n Performing Hilbert transform, and calculating to obtain corresponding envelope momentArray E n ；
Step 4, respectively for envelope line matrix E n Performing Fourier transformation, and calculating to obtain modulation depth matrix D under different modulation frequencies n ；
Step 5, according to the corresponding relation between the modulation frequency and the modulation order, determining a modulation depth matrix O of the 0.5-order modulation order under different rotating speeds based on a peak value maintaining principle n ；
Step 6, drawing a time or rotating speed-critical frequency band cloud chart of 0.5-order modulation depth, and identifying the characteristic frequency band of rough sound of the automobile or the engine through transverse comparison;
the step 1 specifically comprises the following steps:
the engine rotating speed signal and the automobile or engine noise signal are acquired by adopting the same time sampling rate, the noise signals are respectively subjected to band-pass filtering, and the relation between the center frequency and the bandwidth is determined by the following formula:
BW n ＝(25+75×(1+1.4×(f c /1000) 2 ) 0.69 )×ΔBark；
wherein BW is n For critical band bandwidth, n is the number of critical bands, Δbark is the critical band delta, f c Is the critical band center frequency;
obtaining sound signal matrixes BP of different critical frequency bands;
the step 6 specifically comprises the following steps:
and (5) comparing time or rotating speed-critical frequency band cloud pictures of different noise signals with 0.5-order modulation depths, and rapidly identifying and obtaining the characteristic frequency band of rough sound of the automobile or the engine.
2. The method for adaptively identifying a rough audio band according to claim 1, wherein the step 2 specifically comprises:
determining the center point of each data block according to the initial rotation speed and the rotation speed increment, and then determining the start and stop points of each data block according to the time sampling rate and the frequency resolution to obtain sound signal matrixes T of different rotation speed ranges n 。
3. The method for adaptively identifying a coarse audio segment according to claim 2, wherein: the step 3 specifically comprises the following steps:
for T n Hilbert transformation is carried out in a segmented mode, and absolute values are taken as an envelope matrix E n ：
E n ＝|Hilbert[T n ]|。
4. The method for adaptively identifying a rough audio band according to claim 3, wherein the step 4 specifically comprises:
first, for E n Performing FFT conversion in segments, and taking absolute values to obtain amplitude frequency spectrums:
wherein F is n For the corresponding spectrum matrix, A 0 Is an amplitude matrix corresponding to 0Hz, A i For amplitude matrix corresponding to non-zero frequency, f i Represents the analysis frequency, t represents the time,
representing phase;
then according to A 0 And A i Calculating a modulation depth matrix D n ：
5. The method for adaptively identifying a rough audio band according to claim 4, wherein the step 5 specifically comprises:
firstly, determining upper and lower limits of 0.5-order modulation frequency according to a self-defined order width;
then, based on peak hold principle in upper and lower limit frequency range, determining modulation depth matrix O of 0.5 order modulation order at different rotation speeds n 。
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| CN115077690B (en) * | 2022-06-27 | 2024-04-19 | 重庆长安汽车股份有限公司 | Method for evaluating periodic pulsation noise of internal combustion engine |
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