Patent Description:
With rapid development of the Internet technologies and live video technologies, the music effect is added while a short video is played or a live video is performed. In order to improve the user's experience, a video special effect group suitable for a piece of music may be recommended to the user according to the type of the music in the video, and the audio appeal and the visual appeal of the video are strengthened.

<CIT> discloses a system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

XP032768028 discloses application oriented insights into the Gabor transform for acoustic signals processing. The paper presents results of analysis of certain quasi stationary and non-stationary signals using Gabor transform and Gabor spectrogram. Initial results are based on the original programs realizing Gabor transform, whereas the main part of the work - the comparative analysis of signals by Gabor spectrograms of higher orders and other time-frequency distributions was performed using the commercially available software package: Joint Time Frequency Analysis (JTFA) Toolkit from National Instruments.

<CIT> discloses a server system for receiving video clips having an associated audio/musical track for processing at the server system. The system comprises a first beat tracking module for generating a first beat time sequence from the audio signal using an estimation of the signal's tempo and chroma accent information. A ceiling and floor function is applied to the tempo estimation to provide integer versions which are subsequently applied separately to a further accent signal derived from a lower-frequency sub-band of the audio signal to generate second and third beat time sequences. A selection module then compares each of the beat time sequences with the further accent signal to identify a best match.

The objective of the present disclosure is to provide a music classification method, a beat point detection method, a storage device and a computer device.

The present disclosure provides the technical solution as defined by the appended set of claims.

A music classification beat point detection method according to claim <NUM> is provided.

A storage device, according to claim <NUM>, storing a plurality of instructions, wherein the instructions are adapted to be loaded and executed by a processor to perform the music beat point detection method of the above example is provided.

A computer device, including: one or more processors; a memory; and one or more application programs, stored in the memory and configured to be executed by the one or more processors; wherein the one or more application programs is configured to be used for executing the music beat point detection method according to any one of the aforesaid embodiments or is configured to be used for executing the music classification method according to any one of the aforesaid embodiments.

The above and/or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments with reference to the accompanying drawings, in which:.

A description will be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. The reference numbers which are the same or similar throughout the accompanying drawings represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are intended to be illustrative only, and are not to be construed as limitations to the present disclosure.

In the traditional video special effect processing process, beat points of the playing music cannot be obtained, and thus the corresponding video special effect cannot be triggered according to the beat points of the playing music. Therefore, during processing of the video special effect, personalized setting of the special effect cannot be performed according to the playing music in the video, and thus the satisfaction of user experience is influenced.

In the music beat point detection method provided by the present disclosure, the frame processing is performed on a music signal firstly and a power spectrum of each frame signal is obtained, and thus sub-band decomposition is performed on each power spectrum. Time-frequency domain joint filtering is performed on different sub-bands according to beat types corresponding to the sub-bands. To-be-confirmed beat points can be obtained according to filtering results, and then beat points of the music signal is determined according to a power value of each to-be-confirmed beat point. Therefore, the beat points of the music signal can be obtained by the music beat point detection method disclosed by the present disclosure, and thus a video special effect in the special effect group can be triggered in combination with the beat points, and the satisfaction of user experience is improved.

Furthermore, in the music beat point detection method, the beat confidence level of each frequency in each sub-band signal is obtained, and a weighted sum value of the power values corresponding to all the frequencies in each sub-band is calculated by the beat confidence level to obtain the to-be-confirmed beat points according to the weighted sum value. Therefore, the accuracy of the to-be-confirmed beat points can be further improved.

Meanwhile, in the music beat point detection method, the power spectrum of each frame signal is decomposed into a first sub-band used for detecting beat points of a base drum, a second sub-band used for detecting beat points of a snare drum, a third sub-band used for detecting the beat points of the snare drum and a fourth sub-band used for detecting beat points of a high-frequency beat instrument. Therefore, the detection method can perform sub-band decomposition according to types of concrete beat points in the music, and thus the beat points in the music signal can be more accurately detected.

In the following, the technical solutions of the present disclosure will be introduced through several embodiments.

A music beat point detection method and a music beat point based music classification method provided by the present disclosure are applied to an application environment as shown in <FIG>.

As shown in <FIG>, a server100 and clients <NUM> are in one network <NUM> environment and perform data information interaction through the network <NUM>. The number of the server100 and the number of the clients <NUM> are not limited, and the number of the server100 and the number of the clients <NUM> as shown in <FIG> are exemplary only. An APP (Application) is installed in each client <NUM>. A user may perform information interaction with the corresponding server <NUM> by the APP in the client <NUM>.

Each server <NUM> may be, but not limited to, a network server, a management server, an application server, a database server, a cloud server and the like. Each client <NUM> may be, but not limited to, a smart phone, a personal computer (PC), a tablet personal computer, a personal digital assistant (PDA), a mobile Internet device (MID) and the like. An operating system of each client <NUM> may be, but not limited to, Android system, IOS (iPhone operating system), Windows phone system, Windows system and the like.

After the user clicks to select or uploads a piece of music (song) in a video APP of the client <NUM>, the server <NUM> analyzes and estimates the music, further issues and recommends a video special effect group suitable for the music (song) to the client <NUM>, where the user is located, according to an estimated music type and triggers a video special effect in the special effect group at the time position of the estimated beat point. In the music beat point detection method provided by the present disclosure, the beat point of the music uploaded or selected by the user is detected. Therefore, the corresponding video special effect may be triggered according to the beat point of the music, and the satisfaction of user's experience is improved.

The present disclosure provides a music beat point detection method. In one embodiment, as shown in <FIG>, the music beat point detection method of the present disclosure includes the following steps:.

S100, a frame processing is performed on a music signal to obtain frame signals.

In the embodiment, the server obtains the music signal to be detected and performs the frame processing on the music signal to obtain a plurality of frame signals of the music signal. The music signal may be a music signal uploaded by the user or a music signal in a database of the server.

In one embodiment, the server performs preprocessing on the input music signal firstly. The preprocessing process includes the necessary preprocessing operations such as decoding of the input music signal, conversion of dual channel to single channel, sampling rate conversion, removal of direct-current components and the like. The preprocessing process here belongs to normal operation and is not explained in detail here. Furthermore, the server performs frame processing on the music signal which has been performed the preprocessing to obtain a plurality of frame signals.

S200, power spectra of the frame signals are obtained.

In the embodiment, the server further obtains the power spectrum of each frame signal after obtaining the plurality of frame signals of the music signal. Specifically, when the server performs the frame processing on the music signal, N points are one frame, and M points are updated each time (M is smaller than N, M/N is equal to <NUM> to <NUM>), and overlap=N-M.

After the frame processing, a windowing processing is performed on each signal having a frame size of N points, and then FFT (Fast Fourier Transformation) is performed on each signal to obtain the power spectrum P (t, k) of each frame signal. The power spectrum obtaining process belongs to normal operation in signal processing and is not explained in detail here.

S300, sub-band decomposition is performed on the power spectrum, and the power spectrum is decomposed into at least two sub-bands.

In the embodiment, the server performs sub-band decomposition on the power spectrum corresponding to each frame signal and decomposes each power spectrum into at least two sub-bands. Each sub-band is used for detecting a corresponding one type of beat point. Specifically, the server analyzes a frequency spectrum of the music signal and performs the sub-band decomposition on the music signal in combination with the characteristic of the frequency response of a common beat type instrument in music.

In one embodiment, the sub-band decomposition is performed on the power spectrum, and the power spectrum is decomposed into four sub-bands; and the four sub-bands include a first sub-band used for detecting beat points of a base drum, a second sub-band used for detecting beat points of a snare drum, a third sub-band used for detecting the beat points of the snare drum and a fourth sub-band used for detecting beat points of a high-frequency beat instrument. A frequency band of the first sub-band is <NUM> to <NUM>, a frequency band of the second sub-band is <NUM> to <NUM> Hz, a frequency band of the third sub-band is <NUM> Hz to <NUM> Hz, and a frequency band of the fourth sub-band is <NUM> Hz to fs/<NUM>, wherein fs is a sampling frequency of the signal.

In the embodiment, decomposition on a sub-band frequency band of the power spectrum is mainly due to the situation that besides the base drum and the snare drum are greatly different from other beat type instruments (for example, high-frequency beat instruments) in frequency response, durations of different beat type instruments also have large differences, energy of the base drum mainly concentrates on a low frequency sub-band, but non-beat type instruments such as a bass often exist in the low frequency sub-band, and the duration of the bass is much longer than that of the base drum. Energy of the snare drum mainly concentrates on an intermediate frequency sub-band, but a sub-band with a frequency band below <NUM> Hz is disturbed by signals of human voice and the like, and a sub-band with a frequency band above <NUM> Hz is mainly disturbed by other accompaniment musical instruments. The duration of the snare drum is obviously shorter than that of other interference signals on the two intermediate frequency sub-bands, but the duration of an interference signal of the sub-band with the frequency band below <NUM> Hz is obviously different from that of an interference signal of the sub-band with the frequency band above <NUM> Hz, and thus different strategies need to be adopted when the time-frequency domain joint filtering is performed. High frequency sub-bands are often sounds of melodic accompaniment musical instruments having very long durations, which is different from characteristics of the accompaniment musical instruments and human voices occur in the intermediate frequency sub-band.

S400, a time-frequency domain joint filtering is performed on a signal of each sub-band according to a beat type corresponding to each sub-band.

In the embodiment, the server further performs a time-frequency domain joint filtering on the signal of each sub-band according to the beat type corresponding to each sub-band after performing the sub-band decomposition on the power spectrum corresponding to each frame signal. Specifically, the server performs the time-frequency domain joint filtering on the signal of each sub-band by adopting parameters corresponding to beat types according to the detected beat types corresponding to the first sub-band, the second sub-band, the third sub-band and the fourth sub-band when the power spectrum of the frame signal is decomposed into the four sub-bands in the step S300. The parameters corresponding to the beat types are determined as follows: the parameters of the sub-band are set according to characteristics at time and on a harmonic distribution of beat points of beat-like instruments used for detection and other interference signals that are different from the beat points in each sub-band.

In the step, when the server adopts the parameters corresponding to beat types to perform the time-frequency domain joint filtering on the signal of each sub-band, the parameters corresponding to the beat types may be parameters obtained according to the characteristics at time and on a harmonic distribution of beat points of beat-like instruments used for detection and other interference signals that are different from the beat points before the music beat point detection method disclosed by the present disclosure is implemented. Or the parameters corresponding to the beat types may be parameters obtained by the server according to the characteristics at time and on a harmonic distribution of beat points of beat-like instruments used for detection and other interference signals that are different from the beat points while the music beat point detection method disclosed by the present disclosure is implemented.

In the embodiment, the specific steps of time-frequency domain joint filtering may be described as follows:.

Optionally, for different sub-bands, the above operation steps of time-frequency domain joint filtering are the same, but parameter values of hi and hj are different. Selection of the parameters of hi and hj are collectively decided by the characteristics in duration and on harmonic distribution of interference signals of beat type instruments and other melodic interference signals, which fall in different sub-bands. As for each frequency Bin k, the parameters set by the sub-band are selected to filter according to the sub-band to which the frequency Bin k belongs.

Mean filtering, median filtering, Gaussian window filtering or the like may be selected for the smoothing windows wi and wj. In the embodiment of the present disclosure, the frame signals are mainly smoothed (with low-pass filtering) jointly in a time-frequency domain, and other filtering modes may also be adopted in other embodiments.

S500, to-be-confirmed beat points are obtained from the frame signals of the music signal according to a result of the time-frequency domain joint filtering.

In the embodiment, the server may obtain the to-be-confirmed beat points from the frame signals of the music signal according to the result of the time-frequency domain joint filtering. In one embodiment, as shown in <FIG>, the step S500 includes the following steps:.

In one embodiment, the beat confidence level of each frequency and other non-beat melodic beat confidence levels in the signal of each sub-band may be calculated as follows:
as for a signal P (t, k) of a current frame and each frequency k, whether it is a confidence level of one beat (i.e. Wiener filtering) may be given according to the result of the time-frequency domain joint filtering, wherein k represents frequency; and <MAT>.

Accordingly, whether it is the confidence level of one melodic component is as follows: <MAT>.

Furthermore, weighted sum is performed on the signal P (t, k) of the current frame in following manners according the type of the beat point.

P (t, k) is a power spectrum obtained after STFT (Short Time Fourier Transform) is performed on the signal, P (t, k)*B (t, k) embodies weighting of the power spectrum, and B (t, k) represents a confidence level whether the signal is the beat confidence level at a frequency k in a frame t. The confidence level is a numerical value between <NUM> and <NUM>, and is multiplied by the power spectrum of the signal, the power spectrum P (t, k), belonging to a beat, can be kept, and the power spectrum P (t, k), not belonging to the beat, can be inhibited (the numerical value becomes small after the confidence level is multiplied by the power spectrum of the signal).

After weighting, the weighted power spectra are summed, and summation is performed on k according to the sub-band division condition. For example, as for time t=t1, P (t1, k), after STFT analysis, a value range of k is <NUM>-N/<NUM>+<NUM>, that is P (t1, <NUM>), P (t1, <NUM>). P (t1, N/<NUM>+<NUM>) numbers exist, the frequency corresponding to each frequency k is k*fs/N. Therefore, we can also know that k belongs to which sub-band. For example, k belongs to the sub-band <NUM> (base drum sub-band) when it is equal to <NUM>-<NUM>, and k belongs to the sub-band <NUM> (snare drum sub-band) when it is equal to <NUM>-<NUM>, and so on; and then summation of P (t1, <NUM>)*B (t1, <NUM>), P (t1, <NUM>)*B (t1, <NUM>). P (t1, <NUM>) *B (t1, <NUM>) is weighted summation on the sub-band <NUM> (base drum sub-band), and kick (t1) is obtained. The above processing is performed on all the frames would obtain kick (<NUM>), kick (<NUM>). kick (L), and the size of L is decided by the specific length of the music signal.

S600, the beat points of the music signal are obtained according to power values of the to-be-confirmed beat points.

In the embodiment, the server obtains the beat points of the music signal according to the power values corresponding to the beat points, after obtaining the to-be-confirmed beat points. Specifically, as described in the step S500, the server further obtains to-be-confirmed beat points whose weighted sum value is larger than a threshold power value and takes the to-be-confirmed beat points as the beat points of the music signal, after obtaining the weighted sum value of power values corresponding to all the frequencies in each sub-band by calculation. That is, a to-be-confirmed beat point whose weighted sum value is larger than a threshold power value is taken as the beat point of the music signal. The threshold power value is determined as follows: a mean value and a variance of the power values of all the to-be-confirmed beat points are obtained, and a sum value of the mean value and a doubled variance is calculated and serves as the threshold power value. That is, a sum value of the mean value and a doubled variance is taken as the threshold power value.

In a specific embodiment, as for Kick, Snare and Beat (Kick, Snare and Beat are abbreviation expressions of Kick (t), Snare (t) and Beat (t) respectively) obtained in the step S500, they are scanned respectively to find all peak points, and the peak points with the power values larger than the threshold power value T1=mean+std*<NUM> (mean represents a mean value of the power values of all the peak points, and std represents a variance of the power values of all the peak points) are detected beat points. The beat points are marked as the base drum if being detected in Kick, marked as the snare drum if being detected in Snare and marked as other beat points (beat points of a high-frequency beat instrument) if being detected in Beat.

In the music beat point detection method provided by the present disclosure, the frame processing is performed on a music signal firstly and a power spectrum of each frame signal is obtained, and thus sub-band decomposition is performed on the power spectrum. Time-frequency domain joint filtering is performed on different sub-bands according to beat types corresponding to the sub-bands. To-be-confirmed beat points can be obtained according to filtering results, and then beat points of the music signal are determined according to a power value of each to-be-confirmed beat point. Therefore, the beat points of the music signal can be obtained by the music beat point detection method disclosed by the present disclosure, and thus a video special effect in the special effect group can be triggered in combination with the beat points, and the satisfaction of user experience is improved.

Meanwhile, in the music beat point detection method, the power spectrum of each frame signal is decomposed into a first sub-band used for detecting beat points of a base drum, a second sub-band used for detecting beat points of a snare drum, a third sub-band used for detecting the beat points of the snare drum and a fourth sub-band used for detecting beat points of a high-frequency beat instrument. Therefore, the detection method may perform sub-band decomposition according to types of concrete beat points in the music, and thus the beat points in the music signal can be more accurately detected.

In an embodiment, after the step S600, the music beat point detection method further includes:.

Optionally, the strong beat point threshold power value is determined as follows: a mean value and a variance of the power values of all the to-be-confirmed beat points are obtained, and a sum value of the mean value and a triple variance is calculated and serves as the strong beat point threshold power value.

Specifically, as described in the step S600, a beat point with the power value of the peak point larger than a strong beat point threshold power value T2 (T2=mean + std *<NUM>) is the strong beat point; a beat point with the power value of the peak point smaller than the strong beat point threshold power value and larger than or equal to a threshold power value T1 (T1=mean + std *<NUM>) is the weak beat point; and the position of the beat point is a frame t corresponding to the found peak point.

To sum up, as shown in <FIG>, the present disclosure gives the snare drum signal diagram obtained after the step S500 according to an embodiment of the present disclosure. The horizontal axis represents time t, the vertical axis represents power P, and the power P here is the weighted sum value obtained according to the step S500. As shown in <FIG>, a plurality of peaks exist on a signal curve, and all the peak points on the curve may be obtained by scanning. P1 represents the strong beat point threshold power value, and P2 represents the threshold power value. As for the peak points obtained by scanning, the power values of the peak points must be larger than P2 so as to be detected, beats corresponding to the peak points with the power values larger than P2 and smaller than P1 belong to the weak beat points, and beats corresponding to the peak points with the power values larger than P1 belong to the strong beat points; and the peak points with the power value smaller than P2 would be discarded.

According to the solution provided by the present disclosure, the positions of the beat points and the beat types and the music types in the music (song) are analyzed, a very important skeleton in the music, that is, beats are automatically extracted, and triggering times and triggering types of the video special effect are guided by the extracted positions of the beat points, beat types and music types to enable the music to be well combined with the video special effect and to meet people's habits when they see and listen music. This part of work originally required someone to manually mark the beat points and the types in the music and was very tedious. By using the method described by the present disclosure, machine types of the beat points in the music may be automatically marked, and it is found by experiment that the accuracy may reach <NUM> percent or above.

The present disclosure further provides a music classification method based on music beat point. The method includes the steps: the beat points of the music signal are detected by using the music beat point detection method as described in any one of the embodiments; and the music signal is classified according to the number of the beat points in each sub-band.

That classifying the music signal according to the number of the beat points in each sub-band includes: the number of the beat points of the snare drum and the number of the beat points of the base drum in the music signal are counted according to the number of the beat points in each sub-band. The music signal is classified as strong rhythm music if the number of the beat points of the snare drum and the number of the beat points of the base drum are larger than a first threshold; and the music signal is classified as lyric music if the number of the beat points of the base drum is smaller than a second threshold.

Specifically, the music types may be classified by using the number of the aforementioned three types of beat points in the music beat point detection method. The music with the beat points of the snare drum and the beat points of the base drum larger than a threshold <NUM> at the same time is of the type of music with strong rhythm sensation. The music with the beat points of the base drum smaller than a threshold <NUM> is of the type of the lyric music. The threshold <NUM> and the threshold <NUM> are set according to the number of the beat points of the snare drum and the number of the beat points of the base drums in music classification.

In application, the music type is roughly sorted into the two types of the music with strong rhythm sensation and the lyric music, entirely different special effect types may be discriminatively used. Therefore, over intense special effects in the lyric music are avoided from being largely triggered, and the special effects are facilitated to keep consistent with the seeing and listening habits of the people.

The present disclosure further provides a storage device in which a plurality of instructions are stored; the instructions are adapted to be loaded and executed by a processor: the frame processing is performed on the music signal to obtain frame signals; power spectra of the frame signals are obtained; sub-band decomposition is performed on the power spectra, and the power spectrum is decomposed into at least two sub-bands; time-frequency domain joint filtering is performed on a signal of each sub-band according to a beat type corresponding to each sub-band; to-be-confirmed beat points are obtained from the frame signals of the music signal according to a result of the time-frequency domain joint filtering; and the beat points of the music signal are obtained according to power values of the to-be-confirmed beat points;
or the instructions are adapted to be loaded or executed by the processor: the beat points of the music signal are detected by using the music beat point detection method as described in any one of the embodiments; and the music signal is classified according to the number of the beat points in each sub-band.

Furthermore, the storage device may be various media capable of storing program codes, such as a U disk, a mobile hard disk, ROM (Read-Only Memory), a RAM, a disk or an optical disk.

In other embodiments, the instructions in the storage device provided by the present disclosure are loaded by the processor, and the steps described in the music beat point detection method disclosed in any one of the embodiments are executed by the processor. Or, the instructions in the storage device provided by the present disclosure are loaded by the processor, and the music classification method described in any one of the embodiments are executed by the processor.

The present disclosure further provides a computer device. The computer device includes one or more processors, a memory and one or more applications. The one or more applications is stored in the memory, and is configured to be executed by the one or more processors and is configured to be used for executing the music beat point detection method or the music classification method described in any one of the embodiments in the device.

<FIG> is a structural schematic diagram of a computer device according to an embodiment of the present disclosure. The device described in the embodiment may be the computer device, for example, a server, a personal computer and a network device. As shown in <FIG>, the device includes a processor <NUM>, a memory <NUM>, an input unit <NUM> and a display unit <NUM> and other devices. Those skilled in the art may appreciate that the devices of the equipment structure illustrated in <FIG> do not limit all the devices which may include more or fewer components as shown in figures, or have combinations of certain components. The memory <NUM> may be used for storing applications <NUM> and various function modules, the processor <NUM> runs the applications <NUM> stored in the memory <NUM>, and thus various function applications and data processing of the device are executed. The memory may be an internal memory or an external memory or includes both of them. The internal memory may include a read only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM), a flash memory or a random access memory. The external memory may include a hard disk, a floppy disk, a ZIP disk, a U disk, a magnetic tape and the like. The memory disclosed by the present disclosure includes, but not limited to, the memories of these types. The memory disclosed by the present disclosure is given merely as an example and not as a way of limitation.

The input unit <NUM> is used for receiving input of the signals and receiving keywords input by the user. The input unit <NUM> may include a touch panel and other input devices. The touch panel may collect touch operations on or near it (such as the user's operations on or near the touch panel by using any suitable objects or accessories, such as a finger and a stylus, etc.), a corresponding connecting device is driven according to a preset program; and the other input device may include but not limited to one or more of a physical keyboard, function keys (such as a playing control key and a switch button), a trackball, a mouse, an operating lever and the like. The display unit <NUM> may be used for displaying information input by the user or information provided to the user and various menus of the computer device. The display unit <NUM> may take the form of a liquid crystal display, an organic light-emitting diode and the like. The processor <NUM> is a control center of the computer device, the processor <NUM> connects various portions of the whole computer by using various interfaces and lines, and executes various functions and processes data by running or executing software programs and/or modules stored in the memory <NUM> and calling data stored in the memory.

In an embodiment, the device includes one or more processors <NUM>, one or more memories <NUM> and one or more applications <NUM>. The one or more applications <NUM> is stored in the memories <NUM> and is configured to be executed by the one or more processors <NUM> and is configured to be used for executing the music beat point detection method or the music classification method described in the embodiment.

Additionally, various function units in various embodiments of the present disclosure may be integrated into one processing module, each unit may physically exist singly, and two or more units may also be integrated into one processing module. The integrated modules may be implemented in the form of hardware and may also be implemented in the form of a software function module. The integrated modules may be stored in a computer-readable storage medium if being implemented in the form of the software function module and sold or used as an independent product.

Claim 1:
A music beat point detection method, comprising:
performing a frame processing on a music signal to obtain a frame signal (S100);
obtaining a power spectrum of the frame signal (S200);
performing sub-band decomposition on the power spectrum, and decomposing the power spectrum into at least two sub-bands (S300);
performing a time-frequency domain joint filtering on a signal of each sub-band according to a beat type corresponding to each sub-band (S400);
obtaining a beat confidence level of each frequency in a signal of each sub-band according to a result of the time-frequency domain joint filtering (S510);
calculating a weighted sum value of power values corresponding to all frequencies in each sub-band according to the beat confidence level of each frequency (S530);
getting a to-be-confirmed beat point according to the weighted sum value (S550) ; and
obtaining a beat point of the music signal according to a power value of the to-be-confirmed beat point (S600).