CN116156048B - Volume adjustment method, system, equipment and medium based on artificial intelligence - Google Patents
Volume adjustment method, system, equipment and medium based on artificial intelligence Download PDFInfo
- Publication number
- CN116156048B CN116156048B CN202310438061.5A CN202310438061A CN116156048B CN 116156048 B CN116156048 B CN 116156048B CN 202310438061 A CN202310438061 A CN 202310438061A CN 116156048 B CN116156048 B CN 116156048B
- Authority
- CN
- China
- Prior art keywords
- volume
- mobile phone
- user
- determining
- passers
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 27
- 210000005069 ears Anatomy 0.000 claims abstract description 41
- 230000007613 environmental effect Effects 0.000 claims abstract description 24
- 241000287107 Passer Species 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 7
- 230000000284 resting effect Effects 0.000 claims description 5
- 238000013527 convolutional neural network Methods 0.000 description 28
- 238000013528 artificial neural network Methods 0.000 description 20
- 210000002569 neuron Anatomy 0.000 description 12
- 238000003062 neural network model Methods 0.000 description 7
- 230000000306 recurrent effect Effects 0.000 description 6
- 230000004927 fusion Effects 0.000 description 5
- 238000011176 pooling Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 210000004027 cell Anatomy 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 206010011469 Crying Diseases 0.000 description 1
- 206010038743 Restlessness Diseases 0.000 description 1
- 235000013559 Schnittsellerie Nutrition 0.000 description 1
- 244000169997 Schnittsellerie Species 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72457—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to geographic location
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Human Computer Interaction (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Telephone Function (AREA)
Abstract
The invention provides a volume adjustment method, a system, equipment and a medium based on artificial intelligence, which relate to the field of volume adjustment, and the method comprises the steps of determining the distance from a mobile phone to ears of a user by using a distance determination model based on face images of the user and photographing parameters of a front camera of the mobile phone when photographing; determining a minimum volume of the mobile phone by using a minimum volume determination model based on the distance from the mobile phone to the ears of the user and the environmental noise data; determining a plurality of passer-by information using a passer-by information determination model based on a surrounding image of the user; determining the volume bearing degree of the plurality of passers-by using the bearing degree determining model based on the physiological information of the plurality of passers-by; determining the maximum volume of the mobile phone by using a maximum volume determining model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone. The method can automatically adjust the volume and reduce noise pollution in public places.
Description
Technical Field
The invention relates to the technical field of volume adjustment, in particular to a volume adjustment method, system, equipment and medium based on artificial intelligence.
Background
In recent years, the smart phone market is rapidly developed, and the smart phone provides convenient services such as entertainment, communication, information acquisition and the like for users at any time and any place. With the rise of short videos, users can watch various short videos anytime and anywhere to meet entertainment demands, but simultaneously due to the convenience of the short videos, many users can open the short videos in public places to watch, but due to the fact that the users are in the public places, more passers-by exist in the public places, noise pollution can be caused to other surrounding passers-by due to the fact that the sounds of the short videos are too large. The existing method for controlling the volume of the short video and the sound is mostly that when the volume of a user is larger than a certain threshold, the short video application reminds the user to manually turn down the sound through popup window prompt, for example, the popup window prompt can be' the current volume is too large, the influence on other users can be caused, and the user needs to manually adjust and the user cannot turn down the sound according to the prompt, so the method cannot achieve the purposes of volume adjustment and reducing noise pollution in public places.
Therefore, how to automatically adjust the volume and reduce the noise pollution in public places is a problem to be solved at present.
Disclosure of Invention
The invention mainly solves the technical problem of how to automatically adjust the volume and reduce noise pollution in public places.
According to a first aspect, the present invention provides a volume adjustment method based on artificial intelligence, including: acquiring a face image of a user, wherein the face image of the user comprises two ears of the user, and the face image of the user is obtained based on shooting of a front camera of a mobile phone; determining the distance from the mobile phone to the ears of the user by using a distance determination model based on the face image of the user and photographing parameters of the mobile phone when the front camera shoots; determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance from the mobile phone to the ears of the user and the environmental noise data; acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by; determining a plurality of passer-by information using a passer-by information determination model based on the surrounding image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone; determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by; determining the maximum volume of the mobile phone by using a maximum volume determination model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and adjusting the mobile phone volume based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Still further, the physiological information of the passers-by includes the passers-by's age, sex, height, weight, whether resting or not.
Still further, the adjusting the mobile phone volume based on the minimum volume of the mobile phone and the maximum volume of the mobile phone includes: and setting the volume of the mobile phone between the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Still further, the setting the volume of the mobile phone between the minimum volume of the mobile phone and the maximum volume of the mobile phone includes: adding the minimum volume of the mobile phone and the maximum volume of the mobile phone to obtain added volume, dividing the added volume by 2 to obtain target volume, and setting the mobile phone volume as the target volume.
According to a second aspect, the present invention provides an artificial intelligence based volume adjustment system comprising: the mobile phone comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a face image of a user, the face image of the user comprises two ears of the user, and the face image of the user is obtained based on the shooting of a front camera of the mobile phone; the distance determining module is used for determining the distance from the mobile phone to the ears of the user by using a distance determining model based on the face image of the user and photographing parameters of the front camera of the mobile phone when photographing; the minimum volume determining module is used for determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance between the mobile phone and the ears of the user and the environmental noise data; the second acquisition module is used for acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by; the passer information determining module is used for determining a plurality of passer information by using a passer information determining model based on the surrounding environment image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone; the bearing degree determining module is used for determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by; the maximum volume determining module is used for determining the maximum volume of the mobile phone by using a maximum volume determining model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and the adjusting module is used for adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Still further, the physiological information of the passers-by includes the passers-by's age, sex, height, weight, whether resting or not.
Still further, the adjustment module is further configured to: and setting the volume of the mobile phone between the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Still further, the adjustment module is further configured to: adding the minimum volume of the mobile phone and the maximum volume of the mobile phone to obtain added volume, dividing the added volume by 2 to obtain target volume, and setting the mobile phone volume as the target volume.
According to a third aspect, the present invention provides an electronic device comprising: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method described above.
According to a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a program executable by a processor to implement a method as in any of the above aspects.
The invention provides a volume adjustment method, a system, equipment and a medium based on artificial intelligence, wherein the method comprises the steps of determining the distance from a mobile phone to ears of a user by using a distance determination model based on face images of the user and photographing parameters of a front camera of the mobile phone during photographing; determining a minimum volume of the mobile phone by using a minimum volume determination model based on the distance from the mobile phone to the ears of the user and the environmental noise data; determining a plurality of passer-by information using a passer-by information determination model based on a surrounding image of the user; determining the volume bearing degree of the plurality of passers-by using the bearing degree determining model based on the physiological information of the plurality of passers-by; determining the maximum volume of the mobile phone by using a maximum volume determining model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone. The method can automatically adjust the volume and reduce noise pollution in public places.
Drawings
Fig. 1 is a schematic flow chart of a volume adjustment method based on artificial intelligence according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a volume adjustment system based on artificial intelligence according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings by means of specific embodiments.
In an embodiment of the present invention, there is provided a volume adjustment method based on artificial intelligence as shown in fig. 1, where the volume adjustment method based on artificial intelligence includes steps S1 to S8:
step S1, a face image of a user is obtained, wherein the face image of the user comprises ears of the user, and the face image of the user is obtained based on shooting by a front camera of a mobile phone.
The face image of the user is obtained by shooting through a front camera of the mobile phone. The human face image of the user comprises ears of the user, and the position, the outline, the shape and the like of the ears of the user can be clearly displayed in the human face image of the user.
And S2, determining the distance from the mobile phone to the ears of the user by using a distance determination model based on the face image of the user and photographing parameters of the front camera of the mobile phone during photographing.
The photographing parameters of the front camera of the mobile phone during photographing comprise lens multiple, lens angle, resolution and the like of the front camera.
Because the front-facing cameras of the mobile phone take pictures of the user at different distances, the obtained face images of the user are different, and therefore the distance from the mobile phone to the ears of the user during shooting can be processed and determined based on the face images of the user and shooting parameters of the front-facing cameras of the mobile phone during shooting. In some embodiments, the distance of the handset to the user's ears may be determined by a distance determination model.
The distance from the mobile phone to the ears of the user may be the average distance from the mobile phone to the ears, for example, the distance from the mobile phone to the left ear is 1 meter, and the distance from the mobile phone to the right ear is 0.9 meter, and then the average distance from the mobile phone to the ears is 1 meter.
The distance determination model is a convolutional neural network model, which includes a convolutional neural network. Convolutional neural network models are one implementation of artificial intelligence. The Convolutional Neural Network (CNN) may be a multi-layer neural network (e.g., comprising at least two layers). The at least two layers may include at least one of a convolutional layer (CONV), a modified linear unit (ReLU) layer, a pooling layer (POOL), or a fully-connected layer (FC). At least two layers of the Convolutional Neural Network (CNN) may correspond to neurons arranged in three dimensions: width, height, depth. In some embodiments, a Convolutional Neural Network (CNN) may have an architecture of [ input layer-convolutional layer-modified linear cell layer-pooling layer-full-connection layer ]. The convolution layer may calculate the output of neurons connected to a local region in the input, calculate the dot product between the weight of each neuron and its small region connected in the input volume. The input of the distance determination model is the face image of the user and photographing parameters when the front camera of the mobile phone photographs, and the output of the distance determination model is the distance from the mobile phone to the ears of the user.
The distance determination model can be obtained through training through a training sample, the training sample comprises sample input data and labels corresponding to the sample input data, sample input in the training sample comprises face images of sample users, sample photographing parameters of a front camera of the mobile phone during photographing are obtained, and sample output labels in the training sample are sample distances from the mobile phone to ears of the users. The sample output label of the training sample of the distance determination model can be obtained through manual labeling by a worker, for example, the face image of a sample user is manually labeled by sample photographing parameters when a front camera of the mobile phone photographs, and the sample distance from the mobile phone to the ears of the user is labeled. And finally, training an initial distance determination model based on the plurality of training samples to obtain the distance determination model. In some embodiments, the initial distance determination model may be trained by a gradient descent method to obtain a trained distance determination model.
And step S3, determining the minimum volume of the mobile phone by using a minimum volume determination model based on the distance between the mobile phone and the ears of the user and the environmental noise data.
The ambient noise data includes the volume level, volume frequency, etc. of the ambient noise.
The minimum volume of the mobile phone represents the minimum volume that can be guaranteed to be heard by the user. If the volume of the mobile phone is set to be smaller than the minimum volume of the mobile phone, the user cannot hear the sound of the mobile phone. For example, the minimum volume of the mobile phone is 40 db, and if the volume setting of the mobile phone is less than 40 db, the user cannot hear the sound of the mobile phone.
The minimum volume determination model is a deep neural network model, which includes a deep neural network (Deep Neural Networks, DNN). The deep neural network model is one implementation of artificial intelligence. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training. The deep neural network may include a recurrent neural network (Recurrent Neural Network, RNN), a convolutional neural network (Convolutional Neural Networks, CNN), a generating countermeasure network (Generative Adversarial Networks, GAN), and so on. The input of the minimum volume determining model is the distance between the mobile phone and the ears of the user and the environmental noise data, and the output of the minimum volume determining model is the minimum volume of the mobile phone.
In some embodiments, the minimum volume determination model may include a sound propagation loss determination model and a fusion model. The sound propagation loss determination model and the fusion model are both deep neural network models.
The input of the sound propagation loss determination model is air information, and the output of the sound propagation loss determination model is sound propagation loss degree. The air information includes component ratio information, air density information, temperature information, and humidity information of various gases of the air. The sound propagation loss degree indicates the degree of sound propagation loss in air, and the greater the sound propagation loss degree, the greater the sound propagation volume loss in air, and vice versa. The sound propagation loss is mainly affected by the air itself, so that the sound propagation loss can be determined by processing the air information by the sound propagation loss determination model.
The input of the fusion model comprises the sound propagation loss degree, the distance between the mobile phone and the ears of the user and the environmental noise data, the output of the fusion model is the minimum volume of the mobile phone, and the fusion model can comprehensively consider the sound propagation loss degree, the distance between the mobile phone and the ears of the user and the environmental noise data and finally output the minimum volume of the mobile phone.
And S4, acquiring a surrounding image of the user, wherein the surrounding image of the user comprises a plurality of passers-by.
The user's surroundings image may be used to display the user's surroundings information. In some embodiments, the image of the user's surroundings may be taken of the environment by the user's rear camera. In some embodiments, the image of the surrounding environment of the user may be captured by a camera in the environment, and the image of the surrounding environment of the user captured by the camera may be sent to the mobile phone of the user.
Step S5, determining a plurality of pieces of passer information by using a passer information determination model based on the surrounding environment image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances between the plurality of passers-by and the mobile phone.
The plurality of passer-by information comprises physiological information of a plurality of passers-by and distances between the plurality of passers-by and the mobile phone.
The physiological information of the passers-by indicates the age, sex, height, weight, whether or not the passer-by is resting, etc.
The distance from the passer-by to the mobile phone may be a straight line distance from the passer-by to the user mobile phone.
The surrounding image of the user can display a plurality of pieces of passerby information, and the surrounding image of the user can be processed to obtain a plurality of pieces of passerby information.
The passer-by information determining model is a convolutional neural network model, and the convolutional neural network model comprises a convolutional neural network. Convolutional neural network models are one implementation of artificial intelligence. The Convolutional Neural Network (CNN) may be a multi-layer neural network (e.g., comprising at least two layers). The at least two layers may include at least one of a convolutional layer (CONV), a modified linear unit (ReLU) layer, a pooling layer (POOL), or a fully-connected layer (FC). At least two layers of the Convolutional Neural Network (CNN) may correspond to neurons arranged in three dimensions: width, height, depth. In some embodiments, a Convolutional Neural Network (CNN) may have an architecture of [ input layer-convolutional layer-modified linear cell layer-pooling layer-full-connection layer ]. The convolution layer may calculate the output of neurons connected to a local region in the input, calculate the dot product between the weight of each neuron and its small region connected in the input volume. The input of the passer information determination model is an image of the surrounding environment of the user, and the output of the passer information determination model is a plurality of pieces of passer information.
And S6, determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by.
The sound volume bearing degree of the passersby indicates the degree to which the passersby can adapt to the sound volume of the mobile phone when hearing the sound of the mobile phone. The higher the volume bearing degree of the passersby is, the larger the volume can be adapted to, and the larger the volume of the mobile phone of the user can be. The lower the volume bearing degree of the passersby is, the larger the volume of the passersby cannot adapt to the mobile phone, and the smaller the volume of the mobile phone of the user needs to be.
The volume bearing degree of the passersby can be a numerical value between 0 and 1, and the larger the numerical value is, the higher the volume bearing degree of the passersby is, and the mobile phone sound with larger volume can be adapted. The sound volume bearing degree of the passersby is determined by the physiological information of the passersby, for example, when the passersby is an infant with a small age, the sound volume bearing degree is low, and crying can be generated when the mobile phone sounds. For example, by way of an older adult, the hearing of the aged person decreases, and the response to hearing the sound of the mobile phone is small, so that the sound volume is received to a high degree. For another example, when a passer-by takes a rest, a restless emotion is generated after hearing the sound of the mobile phone, and the volume of the passer-by taking the rest is born to a lower degree, and vice versa.
The tolerance level determination model is a deep neural network model comprising a deep neural network (Deep Neural Networks, DNN). The deep neural network model is one implementation of artificial intelligence. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training. The deep neural network may include a recurrent neural network (Recurrent Neural Network, RNN), a convolutional neural network (Convolutional Neural Networks, CNN), a generating countermeasure network (Generative Adversarial Networks, GAN), and so on. The input of the bearing degree determining model is the information of the plurality of passers-by, and the output of the bearing degree determining model is the volume bearing degree of the plurality of passers-by.
The bearing degree determining model can be obtained through training through a training sample, the training sample comprises sample input data and labels corresponding to the sample input data, sample input in the training sample comprises a plurality of sample passerby information, sample output labels in the training sample are the volume bearing degree of the plurality of sample passerby information, and sample output labels in the training sample can be obtained through manual labeling.
And S7, determining the maximum volume of the mobile phone by using a maximum volume determination model based on the volume bearing degree of the passers-by, the distances between the passers-by and the mobile phone and the environmental noise data.
The maximum volume determination model is a deep neural network model, which includes a deep neural network (Deep Neural Networks, DNN). The deep neural network model is one implementation of artificial intelligence. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training. The deep neural network may include a recurrent neural network (Recurrent Neural Network, RNN), a convolutional neural network (Convolutional Neural Networks, CNN), a generating countermeasure network (Generative Adversarial Networks, GAN), and so on. The input of the maximum volume determining model is the volume bearing degree of the passers-by and the distance between the passers-by and the mobile phone, and the output of the maximum volume determining model is the maximum volume of the mobile phone.
The maximum volume determining model can comprehensively consider the volume bearing degree of the passers-by, the distance between the passers-by and the mobile phone and the environmental noise data to determine the maximum volume of the mobile phone.
The maximum volume of the mobile phone represents the maximum volume which has the smallest influence on a plurality of surrounding passers-by and is determined by comprehensively considering the volume bearing degree of a plurality of passers-by and the distance between the plurality of passers-by and the mobile phone. If the volume set by the mobile phone exceeds the maximum volume of the mobile phone, discomfort may be caused to a plurality of passers-by around the user.
And S8, adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
In some embodiments, the volume of the mobile phone can be set between the minimum volume of the mobile phone and the maximum volume of the mobile phone, so that the user can hear the sound in the mobile phone, and the influence on a plurality of surrounding passers-by is reduced.
In some embodiments, a mobile phone volume of the user may be randomly selected from a minimum volume of the mobile phone and a maximum volume of the mobile phone.
In some embodiments, the minimum volume of the mobile phone and the maximum volume of the mobile phone may be added to obtain an added volume, the added volume is divided by 2 to obtain a target volume, and the mobile phone volume is set as the target volume. As an example, if the minimum volume of the mobile phone is 40 and the maximum volume of the mobile phone is 100, then (40+100)/(2=70), the mobile phone volume may be set to 70 db.
Based on the same inventive concept, fig. 2 is a schematic diagram of an artificial intelligence-based volume adjustment system according to an embodiment of the present invention, where the artificial intelligence-based volume adjustment system includes:
a first obtaining module 21, configured to obtain a face image of a user, where the face image of the user includes two ears of the user, and the face image of the user is obtained based on a front camera of a mobile phone;
the distance determining module 22 is configured to determine a distance from the mobile phone to the ears of the user using a distance determining model based on the face image of the user and photographing parameters of the front camera of the mobile phone when photographing;
a minimum volume determining module 23, configured to determine a minimum volume of the mobile phone using a minimum volume determining model based on the distance between the mobile phone and the ears of the user and the environmental noise data;
a second obtaining module 24, configured to obtain an image of a surrounding environment of a user, where the image of the surrounding environment of the user includes a plurality of passers-by;
a passer information determination module 25 for determining a plurality of passer information using a passer information determination model based on an image of the surrounding environment of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone;
a bearing degree determining module 26 for determining the volume bearing degree of the plurality of passers-by using a bearing degree determining model based on the physiological information of the plurality of passers-by;
a maximum volume determining module 27, configured to determine a maximum volume of the mobile phone using a maximum volume determining model based on the volume bearing degrees of the plurality of passers-by, the distances between the plurality of passers-by and the mobile phone, and the environmental noise data;
and the adjusting module 28 is used for adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, as shown in fig. 3, including:
a processor 31; a memory 32; a computer program; wherein the computer program is stored in the memory 32 and configured to be executed by the processor 31 to implement the artificial intelligence based volume adjustment method, the method comprising: acquiring a face image of a user, wherein the face image of the user comprises two ears of the user, and the face image of the user is obtained based on shooting of a front camera of a mobile phone; determining the distance from the mobile phone to the ears of the user by using a distance determination model based on the face image of the user and photographing parameters of the mobile phone when the front camera shoots; determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance from the mobile phone to the ears of the user and the environmental noise data; acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by; determining a plurality of passer-by information using a passer-by information determination model based on the surrounding image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone; determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by; determining the maximum volume of the mobile phone by using a maximum volume determination model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and adjusting the mobile phone volume based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Based on the same inventive concept, the present embodiment provides a computer readable storage medium having a program stored thereon, the program being executable by a processor 31 to implement the artificial intelligence based volume adjustment method provided as described above, the method comprising acquiring a face image of a user, the face image of the user including both ears of the user, the face image of the user being captured based on a front camera of a mobile phone; determining the distance from the mobile phone to the ears of the user by using a distance determination model based on the face image of the user and photographing parameters of the mobile phone when the front camera shoots; determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance from the mobile phone to the ears of the user and the environmental noise data; acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by; determining a plurality of passer-by information using a passer-by information determination model based on the surrounding image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone; determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by; determining the maximum volume of the mobile phone by using a maximum volume determination model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data; and adjusting the mobile phone volume based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. An artificial intelligence based volume adjustment method, comprising:
acquiring a face image of a user, wherein the face image of the user comprises two ears of the user, and the face image of the user is obtained based on shooting of a front camera of a mobile phone;
determining the distance from the mobile phone to the ears of the user by using a distance determination model based on the face image of the user and photographing parameters of the mobile phone when the front camera shoots;
determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance from the mobile phone to the ears of the user and the environmental noise data;
acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by;
determining a plurality of passer-by information using a passer-by information determination model based on the surrounding image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone;
determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by;
determining the maximum volume of the mobile phone by using a maximum volume determination model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data;
and adjusting the mobile phone volume based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
2. The artificial intelligence based volume adjusting method as claimed in claim 1, wherein the physiological information of the passer-by includes age, sex, height, weight, whether the passer-by is resting or not.
3. The artificial intelligence based volume adjustment method of claim 1, wherein the adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone comprises: and setting the volume of the mobile phone between the minimum volume of the mobile phone and the maximum volume of the mobile phone.
4. The artificial intelligence based volume adjustment method of claim 3, wherein the setting the volume of the handset between the minimum volume of the handset and the maximum volume of the handset comprises: adding the minimum volume of the mobile phone and the maximum volume of the mobile phone to obtain added volume, dividing the added volume by 2 to obtain target volume, and setting the mobile phone volume as the target volume.
5. An artificial intelligence based volume adjustment system, comprising:
the mobile phone comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a face image of a user, the face image of the user comprises two ears of the user, and the face image of the user is obtained based on the shooting of a front camera of the mobile phone;
the distance determining module is used for determining the distance from the mobile phone to the ears of the user by using a distance determining model based on the face image of the user and photographing parameters of the front camera of the mobile phone when photographing;
the minimum volume determining module is used for determining the minimum volume of the mobile phone by using a minimum volume determining model based on the distance between the mobile phone and the ears of the user and the environmental noise data;
the second acquisition module is used for acquiring a surrounding image of a user, wherein the surrounding image of the user comprises a plurality of passers-by;
the passer information determining module is used for determining a plurality of passer information by using a passer information determining model based on the surrounding environment image of the user; the plurality of passer-by information comprises physiological information of a plurality of passers-by and distances from the plurality of passers-by to the mobile phone;
the bearing degree determining module is used for determining the volume bearing degree of the passers-by using a bearing degree determining model based on the physiological information of the passers-by;
the maximum volume determining module is used for determining the maximum volume of the mobile phone by using a maximum volume determining model based on the volume bearing degree of the plurality of passers-by, the distance between the plurality of passers-by and the mobile phone and the environmental noise data;
and the adjusting module is used for adjusting the volume of the mobile phone based on the minimum volume of the mobile phone and the maximum volume of the mobile phone.
6. The artificial intelligence based volume adjustment system of claim 5, wherein the physiological information of the passer-by includes age, sex, height, weight, whether the passer-by is resting.
7. The artificial intelligence based volume adjustment system of claim 5, wherein the adjustment module is further configured to: and setting the volume of the mobile phone between the minimum volume of the mobile phone and the maximum volume of the mobile phone.
8. The artificial intelligence based volume adjustment system of claim 5, wherein the adjustment module is further configured to: adding the minimum volume of the mobile phone and the maximum volume of the mobile phone to obtain added volume, dividing the added volume by 2 to obtain target volume, and setting the mobile phone volume as the target volume.
9. An electronic device, comprising: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the artificial intelligence based volume adjustment method of any one of claims 1 to 4.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the artificial intelligence based volume adjustment method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310438061.5A CN116156048B (en) | 2023-04-23 | 2023-04-23 | Volume adjustment method, system, equipment and medium based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310438061.5A CN116156048B (en) | 2023-04-23 | 2023-04-23 | Volume adjustment method, system, equipment and medium based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116156048A CN116156048A (en) | 2023-05-23 |
| CN116156048B true CN116156048B (en) | 2023-12-08 |
Family
ID=86339292
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310438061.5A Active CN116156048B (en) | 2023-04-23 | 2023-04-23 | Volume adjustment method, system, equipment and medium based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116156048B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362848B (en) * | 2023-06-03 | 2023-10-27 | 广州爱特安为科技股份有限公司 | Electronic commerce recommendation method, system, equipment and medium based on artificial intelligence |
| CN117520788B (en) * | 2024-01-05 | 2024-03-22 | 成都亚度克升科技有限公司 | Sound box parameter determining method and system based on artificial intelligence and big data analysis |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105721657A (en) * | 2016-01-22 | 2016-06-29 | 深圳市智汇十方科技有限公司 | Method and device for adjusting audio parameters of telephone receiver |
| CN106161801A (en) * | 2016-08-17 | 2016-11-23 | 珠海格力电器股份有限公司 | Call volume adjusting method and device of mobile terminal |
| US9571628B1 (en) * | 2015-11-13 | 2017-02-14 | International Business Machines Corporation | Context and environment aware volume control in telephonic conversation |
| CN106488036A (en) * | 2016-11-29 | 2017-03-08 | 深圳天珑无线科技有限公司 | Volume adjusting method and device |
| CN108777827A (en) * | 2018-06-14 | 2018-11-09 | Oppo广东移动通信有限公司 | Wireless headset, method for regulation of sound volume and Related product |
| CN108834030A (en) * | 2018-09-28 | 2018-11-16 | 广州小鹏汽车科技有限公司 | A kind of vehicle interior sound field adjusting method and audio system |
| CN110995925A (en) * | 2019-11-27 | 2020-04-10 | 广东以诺通讯有限公司 | Method, system, mobile terminal and storage medium for adjusting volume |
| CN113495712A (en) * | 2020-03-20 | 2021-10-12 | 北京字节跳动网络技术有限公司 | Automatic volume adjustment method, apparatus, medium, and device |
| CN113497852A (en) * | 2020-04-07 | 2021-10-12 | 北京字节跳动网络技术有限公司 | Automatic volume adjustment method, apparatus, medium, and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140329567A1 (en) * | 2013-05-01 | 2014-11-06 | Elwha Llc | Mobile device with automatic volume control |
| US9699580B2 (en) * | 2015-09-28 | 2017-07-04 | International Business Machines Corporation | Electronic media volume control |
| US10481856B2 (en) * | 2017-05-15 | 2019-11-19 | Microsoft Technology Licensing, Llc | Volume adjustment on hinged multi-screen device |
-
2023
- 2023-04-23 CN CN202310438061.5A patent/CN116156048B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9571628B1 (en) * | 2015-11-13 | 2017-02-14 | International Business Machines Corporation | Context and environment aware volume control in telephonic conversation |
| CN105721657A (en) * | 2016-01-22 | 2016-06-29 | 深圳市智汇十方科技有限公司 | Method and device for adjusting audio parameters of telephone receiver |
| CN106161801A (en) * | 2016-08-17 | 2016-11-23 | 珠海格力电器股份有限公司 | Call volume adjusting method and device of mobile terminal |
| CN106488036A (en) * | 2016-11-29 | 2017-03-08 | 深圳天珑无线科技有限公司 | Volume adjusting method and device |
| CN108777827A (en) * | 2018-06-14 | 2018-11-09 | Oppo广东移动通信有限公司 | Wireless headset, method for regulation of sound volume and Related product |
| CN108834030A (en) * | 2018-09-28 | 2018-11-16 | 广州小鹏汽车科技有限公司 | A kind of vehicle interior sound field adjusting method and audio system |
| CN110995925A (en) * | 2019-11-27 | 2020-04-10 | 广东以诺通讯有限公司 | Method, system, mobile terminal and storage medium for adjusting volume |
| CN113495712A (en) * | 2020-03-20 | 2021-10-12 | 北京字节跳动网络技术有限公司 | Automatic volume adjustment method, apparatus, medium, and device |
| CN113497852A (en) * | 2020-04-07 | 2021-10-12 | 北京字节跳动网络技术有限公司 | Automatic volume adjustment method, apparatus, medium, and device |
Non-Patent Citations (1)
| Title |
|---|
| 移动终端音量调节专利技术分析;冯晨露等;中国科技信息;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116156048A (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116156048B (en) | Volume adjustment method, system, equipment and medium based on artificial intelligence | |
| CN110148102B (en) | Image synthesis method, advertisement material synthesis method and device | |
| CN109543714B (en) | Data feature acquisition method and device, electronic equipment and storage medium | |
| CN105631408B (en) | Face photo album processing method and device based on video | |
| US7154510B2 (en) | System and method for modifying a portrait image in response to a stimulus | |
| WO2019137167A1 (en) | Photo album management method and apparatus, storage medium, and electronic device | |
| WO2018120662A1 (en) | Photographing method, photographing apparatus and terminal | |
| JP7514848B2 (en) | Image display with selective motion rendering | |
| CN105825486A (en) | Beautifying processing method and apparatus | |
| CN111510630B (en) | Image processing method, device and storage medium | |
| CN110677591B (en) | Sample set construction method, image imaging method, device, medium and electronic equipment | |
| US20200066297A1 (en) | Audio event detection method and device, and computer-readable storage medium | |
| CN107734251A (en) | A kind of photographic method and mobile terminal | |
| CN110930329B (en) | Star image processing method and device | |
| CN108200337B (en) | Photographing processing method, device, terminal and storage medium | |
| JP6096654B2 (en) | Image recording method, electronic device, and computer program | |
| WO2020093798A1 (en) | Method and apparatus for displaying target image, terminal, and storage medium | |
| WO2020133409A1 (en) | Photography method and photographic terminal | |
| JP2023512966A (en) | Image processing method, electronic device and computer readable storage medium | |
| CN109104578A (en) | A kind of image processing method and mobile terminal | |
| CN109986553A (en) | Active interaction robot, system, method and storage device | |
| US20130308829A1 (en) | Still image extraction apparatus | |
| CN117670655A (en) | Image generation method, device, electronic equipment, storage medium and chip | |
| US8913142B2 (en) | Context aware input system for focus control | |
| CN110084306B (en) | Method and apparatus for generating dynamic image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231115 Address after: No. 49, Shangmao 1st Road, Encheng Street, Enping City, Jiangmen City, Guangdong Province, 529400 Applicant after: Enping Huadian Electronics Co.,Ltd. Address before: No. 118, West Section of Nanpu Road, Liucheng Town, Wenjiang District, Chengdu City, Sichuan Province, 611100, 4th Floor, Building 1, Nanpu County Applicant before: Chengdu Sufu Software Development Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Volume adjustment method, system, device, and medium based on artificial intelligence Granted publication date: 20231208 Pledgee: China Postal Savings Bank Co.,Ltd. Enping City Branch Pledgor: Enping Huadian Electronics Co.,Ltd. Registration number: Y2024980041720 |