Intelligent music management system based on motion stride frequency detection

Abstract

The invention relates to the technical field of sports equipment, and particularly discloses an intelligent music management system based on sports stride frequency detection, which comprises a stride frequency detection unit, an audio recognition unit, a feature matching unit, a main control module, a playing management unit and a control detection unit, the audio recognition unit is used for acquiring music data, extracting time-frequency features and analyzing music speed and rhythm; the feature matching unit is used for receiving stride frequency information and audio information and matching the stride frequency information and the audio information; data of stride frequency analysis and audio analysis are combined, music suitable for bpm is introduced for a runner to relieve subjective fatigue in exercise when the runner exercises, the system can operate independently through a local sound source database, when a local sound source is insufficient, the system is connected with a cloud server through the internet to achieve music resource acquisition, and the system is convenient to use. The current music playing data is recorded when the user finishes each exercise, so that the user can conveniently and quickly manage the music which is not frequently used.

Description

Technical Field

[0001] The present invention belongs to the technical field of sports equipment, and in particular relates to an intelligent music management system based on sports step frequency detection. Background Art

[0002] With the development of modern society, residents' fitness and health issues have become a national social topic. The focus is on smart sports, but the current domestic smart sports apps cannot meet the needs of exercisers. During exercise, there is a large difference between the music BPM and the runner's step frequency, which does not promote the runner's exercise, but interferes with the runner's exercise rhythm, reduces exercise efficiency and other negative effects.

[0003] From the perspective of modern medicine, introducing the rhythm of music during exercise can increase the excitement level of the cerebral cortex, relieve subjective fatigue during exercise, and improve mood. Appropriate music can make it easier for running enthusiasts to find the rhythm of exercise and quickly enter the running state, reducing fatigue and self-loneliness during running, and increasing the pleasure and sense of participation in running.

[0004] At present, when recommending running music, most sports apps are unable to cope with the dynamic changes and volatility of users' exercise status. There is no recommendation technology that takes internal factors as the starting point, that is, makes recommendations based on people's actual conditions. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent music management system based on sports pace detection to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent music management system based on motion cadence detection includes: a cadence detection unit, an audio recognition unit, a feature matching unit, a main control module, a playback management unit, and a control detection unit. The cadence detection is used to obtain user travel data and analyze user gait information. The audio recognition is used to obtain music data, extract time-frequency features, and analyze music speed and beat. The feature matching unit is used to receive cadence information and audio information and match the two. The main control module is used to control the operating status of other functional areas and cooperate with other functional areas to achieve motion assistance. The playback management unit is used to cooperate with the user's motion status to play music and perform related management and control. The control detection unit is used to obtain the user's operation information and then generate an execution command to send to the target area.

[0008] Preferably, the cadence detection unit processes the user behavior status information based on singular spectrum analysis, the user behavior status information is based on the mobile phone acceleration signal obtained by the built-in acceleration sensor of the mobile phone when the user is exercising, and the mobile phone acceleration signal is subjected to noise reduction analysis through singular spectrum analysis to obtain the user gait feature analysis result, the cadence detection unit calls the stored user historical cadence processing information to assist in analyzing the current gait features and provide basic information for the analysis of changes in gait features.

[0009] Preferably, the process of determining the user's cadence in the cadence detection unit: using the signal stratification and denoising method of SSA, the acceleration modulus, zero bias and random error are regarded as trend items, and the acceleration-related signal related to the cadence is used as a periodic item. By extracting the periodic item related to the cadence, the walking cadence of pedestrians at different carrying positions of the mobile phone is obtained, and since one step is from the heel of one foot being lifted to the heel of the other foot touching the ground during walking, the acceleration in the vertical and horizontal directions is roughly a sinusoidal signal, the acceleration modulus after preprocessing is taken to obtain the sinusoidal signal of the pedestrian's walking, and the peak detection method is used, and each consecutive detection of two peaks is regarded as a step, so that the dynamic cadence can be captured quickly and efficiently from the movement process.

[0010] Preferably, the audio recognition unit uses built-in library functions of libraries such as Librosa in Python to extract time-frequency features of music. The audio recognition unit also performs secondary middle-level feature extraction on the music through the spectrum diagram, and analyzes the extracted features together with the time-frequency features of the music to obtain music speed information. Then, based on the music speed information, the music beat information is determined by function analysis data. The audio recognition unit matches the music beat information with the user gait feature analysis results to determine the music beat information that is suitable for the current user's motion state, and then confirms the optional environmental music information through the reverse extension of the data analysis.

[0011] Preferably, the operation process of the music content in the audio recognition unit is as follows: perform spectral analysis on the acquired music, obtain the starting point of the note by detecting the phase change of the spectrum, calculate the correlation coefficient of the starting point detection function of each frame, estimate the beat period based on the context, and predict the specific time point when the beat occurs in combination with the hidden Markov model. The tracking algorithm assigns a state to each point, and each state generates an observation to form an observation sequence, on this basis, a prediction model is formed to effectively mark the accuracy of the music bpm.

[0012] Preferably, the feature matching unit further combines the music beat information and the user gait feature analysis results for analysis, calls the user's recent music playback history information and matches it with the music information played during previous exercise to provide basic information for further analysis, and the feature matching unit selects the music content that can be played as the current user's exercise state and distributes it to the playback management unit.

[0013] Preferably, the playback management unit and the control detection unit cooperate with each other to work together. After the playback management unit selects one of the songs from the screened music content to play, the control detection unit detects in real time whether the user issues an operation instruction (such as by touch, voice, etc.), and after receiving the user's operation instruction, sends a corresponding execution command to enable the playback management unit to change the state of music playback. The control detection unit can adjust the state of the main control module, audio recognition unit, etc. according to the user's operation instruction.

[0014] Preferably, the system also includes a network connection unit and an information collection unit. The network connection unit is used to establish a stable network connection and provide information resources to other functional areas through the network. The information collection unit is used to collect user usage data and user identity information and store backups, and determine the usage rights of some special music content through user identity information. The information collection unit also includes user pre-stored music information and associated user usage setting information.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention combines the data of step frequency analysis and audio analysis to introduce music with appropriate bpm to runners during exercise to relieve subjective fatigue during exercise and improve mood. The system can operate independently through a local sound source database. When the local sound source is insufficient, the cloud server is connected to the Internet to obtain music resources. The current music playback data is recorded at the end of each exercise by the user, which can facilitate the user to quickly manage music that is not frequently used, track the user's exercise and realize frequency matching, music start, pause and switching in real time. The music actively cooperates with the user's exercise rhythm, thereby improving the user's satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system framework diagram of the present invention;

[0018] Figure 2 This is a structural diagram of the pedestrian cadence detection of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example:

[0021] See also Figure 1-Figure 2 As shown, an intelligent music management system based on motion cadence detection includes: a cadence detection unit, an audio recognition unit, a feature matching unit, a main control module, a playback management unit, and a control detection unit. The cadence detection is used to obtain user travel data and analyze user gait information. The audio recognition is used to obtain music data, extract time-frequency features, and analyze music speed and beat. The feature matching unit is used to receive cadence information and audio information and match the two. The main control module is used to control the operating status of other functional areas and cooperate with other functional areas to achieve motion assistance. The playback management unit is used to cooperate with the user's motion status to play music and perform related management and control. The control detection unit is used to obtain the user's operation information and then generate an execution command to send to the target area.

[0022] The cadence detection unit processes the user behavior status information based on singular spectrum analysis. The user behavior status information is based on the mobile phone acceleration signal obtained by the built-in accelerometer of the mobile phone when the user is exercising. The mobile phone acceleration signal is subjected to noise reduction analysis through singular spectrum analysis to obtain the user gait feature analysis result. The cadence detection unit calls the stored user historical cadence processing information to assist in analyzing the current gait features and provide basic information for the analysis of gait feature changes.

[0023] The process of determining the user's cadence in the cadence detection unit: Using the signal stratification and denoising method of SSA, the acceleration modulus, zero bias and random error are regarded as trend items, and the acceleration-related signal related to the cadence is regarded as the periodic item. By extracting the periodic item related to the cadence, the walking cadence of pedestrians at different carrying positions of the mobile phone is obtained. Since one step is from the heel of one foot being lifted to the heel of the other foot touching the ground during walking, the acceleration in the vertical and horizontal directions is roughly a sinusoidal signal. The acceleration modulus after preprocessing is taken to obtain the sinusoidal signal of the pedestrian's walking. The peak detection method is used, and each consecutive detection of two peaks is regarded as a step, so that the dynamic cadence can be captured quickly and efficiently from the movement process.

[0024] The audio recognition unit uses the built-in library functions of the Librosa and other libraries in Python to extract the time-frequency features of music. The audio recognition unit also extracts secondary middle-level features of the music through the spectrum diagram, and analyzes the extracted features together with the time-frequency features of the music to obtain the music speed information. Then, based on the music speed information, the music beat information is determined through function analysis data. The audio recognition unit matches the music beat information with the user's gait feature analysis results to determine the music beat information that is suitable for the current user's motion state, and then confirms the optional environmental music information through the reverse extension of the data analysis.

[0025] The operation process of music content in the audio recognition unit is as follows: perform spectral analysis on the acquired music, then obtain the starting point of the note by detecting the phase change of the spectrum, calculate the correlation coefficient of the starting point detection function of each frame, estimate the beat period based on the context, and combine the hidden Markov model to predict the specific time point when the beat occurs. The tracking algorithm assigns a state to each point, and each state generates an observation to form an observation sequence. On this basis, a prediction model is formed to effectively mark the accuracy of the music BPM.

[0026] The feature matching unit further combines the music beat information and the user gait feature analysis results for analysis, calls the user's recent music playback history information and matches it with the music information played during previous exercise to provide basic information for further analysis. The feature matching unit selects the music content that can be played as the current user's exercise state and distributes it to the playback management unit.

[0027] The playback management unit and the control detection unit work together to play a role. After the playback management unit selects one of the selected music contents to play, the control detection unit detects in real time whether the user issues an operation instruction (such as through touch, voice, etc.). After receiving the user's operation instruction, the corresponding execution command is sent to enable the playback management unit to change the status of the music playback. The control detection unit can adjust the status of the main control module, audio recognition unit, etc. according to the user's operation instruction.

[0028] The system also includes a network connection unit and an information collection unit. The network connection unit is used to establish a stable network connection and provide information resources to other functional areas through the network. The information collection unit is used to collect user usage data and user identity information and store backups. The use rights of some special music content are determined through user identity information. The information collection unit also includes user pre-stored music information and associated user usage setting information.

[0029] As can be seen from the above, the present invention combines the data of step frequency analysis and audio analysis to introduce music with appropriate bpm for runners during exercise to relieve subjective fatigue during exercise and improve mood. The system can operate independently through the local sound source database. When the local sound source is insufficient, the cloud server is connected to the Internet to obtain music resources. The current music playback data is recorded after each exercise of the user, which can facilitate the user to quickly manage music that is not frequently used, track the user's exercise and realize music frequency matching, music start, pause and switch in real time. The music actively cooperates with the user's exercise rhythm, thereby improving the user's satisfaction.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent music management system based on sports frequency detection, characterized in that: include: A cadence detection unit, an audio recognition unit, a feature matching unit, a main control module, a playback management unit, and a manipulation detection unit. The cadence detection unit is used to obtain user travel data and analyze user gait information. The audio recognition unit is used to obtain music data, extract time-frequency features, and analyze music speed and beat. The feature matching unit is used to receive cadence information and audio information and match the two. The main control module is used to control the operating status of other functional areas and cooperate with other functional areas to achieve motion assistance. The playback management unit is used to cooperate with the user's motion status to play music and perform related management and control. The manipulation detection unit is used to obtain the user's operation information and then generate an execution command to send to the target area.

2. The intelligent music management system based on sports step frequency detection according to claim 1 is characterized in that: The cadence detection unit processes the user behavior status information based on singular spectrum analysis. The user behavior status information is based on the mobile phone acceleration signal obtained by the built-in acceleration sensor of the mobile phone when the user is exercising. The mobile phone acceleration signal is subjected to noise reduction analysis through singular spectrum analysis to obtain the user gait feature analysis result. The cadence detection unit calls the stored user historical cadence processing information to assist in analyzing the current gait features and provide basic information for the analysis of changes in gait features.

3. The intelligent music management system based on sports frequency detection according to claim 1 or 2, characterized in that: The process of determining the user's cadence in the cadence detection unit is as follows: using the signal stratification and denoising method of SSA, the acceleration modulus, zero bias and random error are regarded as trend items, and the acceleration-related signal related to the cadence is regarded as a periodic item. By extracting the periodic item related to the cadence, the walking cadence of the pedestrian at different carrying positions of the mobile phone is obtained. Since one step is from the heel of one foot being lifted to the heel of the other foot touching the ground during walking, the acceleration in the vertical and horizontal directions is roughly a sinusoidal signal. The acceleration modulus after preprocessing is taken to obtain the sinusoidal signal of the pedestrian's walking. The peak detection method is used, and each consecutive detection of two peaks is regarded as a step, so that the dynamic cadence can be captured quickly and efficiently from the movement process.

4. The intelligent music management system based on sports frequency detection according to claim 1 or 2, characterized in that: The audio recognition unit uses the built-in library functions of the Librosa library and other libraries in Python to extract the time-frequency features of music. The audio recognition unit also performs secondary middle-level feature extraction on the music through the spectrum diagram, and analyzes the extracted features together with the time-frequency features of the music to obtain music speed information. Then, based on the music speed information, the music beat information is determined through function analysis data. The audio recognition unit matches the music beat information with the user gait feature analysis results to determine the music beat information that is suitable for the current user's motion state, and then confirms the optional environmental music information through the reverse extension of the data analysis.

5. The intelligent music management system based on sports frequency detection according to claim 1 or 4, characterized in that: The operation process of the music content in the audio recognition unit is as follows: spectrum analysis is performed on the acquired music, and then the starting point of the note is obtained by detecting the phase change of the spectrum, the correlation coefficient of the starting point detection function of each frame is calculated, the beat period is estimated based on the context, and the specific time point when the beat occurs is predicted in combination with the hidden Markov model. The tracking algorithm assigns a state to each point, and each state generates an observation to form an observation sequence. On this basis, a prediction model is formed to effectively mark the accuracy of the music BPM.

6. The intelligent music management system based on sports frequency detection according to claim 1 or 4, characterized in that: The feature matching unit further combines the music beat information and the user gait feature analysis results for analysis, calls the user's recent music playback history information and matches it with the music information played during previous exercise to provide basic information for further analysis, and the feature matching unit selects the music content that can be played as the current user's exercise state and distributes it to the playback management unit.

7. The intelligent music management system based on sports frequency detection according to claim 1 or 6, characterized in that: The playback management unit and the control detection unit cooperate with each other to work together. After the playback management unit selects one of the songs from the screened music content to play, the control detection unit detects in real time whether the user issues an operation instruction (such as by touch, voice, etc.). After receiving the user's operation instruction, the corresponding execution command is sent to enable the playback management unit to change the status of the music playback. The control detection unit can adjust the status of the main control module, audio recognition unit, etc. according to the user's operation instruction.

8. The intelligent music management system based on sports frequency detection according to claim 1 is characterized in that: The system also includes a network connection unit and an information collection unit. The network connection unit is used to establish a stable network connection and provide information resources to other functional areas through the network. The information collection unit is used to collect user usage data and user identity information and store backups, and determine the use rights of some special music content through user identity information. The information collection unit also includes user pre-stored music information and associated user usage setting information.

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