GB2598920A

GB2598920A - A method and a system for controlling a customized playback of sound files based on playlist scoring

Info

Publication number: GB2598920A
Application number: GB2014715.3A
Authority: GB
Inventors: O'donnell Lee
Original assignee: Daimler AG
Current assignee: Mercedes Benz Group AG
Priority date: 2020-09-18
Filing date: 2020-09-18
Publication date: 2022-03-23
Also published as: GB202014715D0

Abstract

Playback of sound files for a user in a vehicle is customized. User preference data regarding a plurality of sound files is provided. Environment data 3 relating to an environment of the vehicle and/or a ride of the vehicle and/or receiving time data 4 which specifies a point in time for the sound files to be played is received. A control signal for controlling the playback of the sound files from at least the user preference data, the environment data and the time data is output over a control interface. A respective score for a plurality of sound files is determined based on playback operation data for each of the sound files where playback operation data is based on operational action 7 during former playback. Based on the environment data, the time data and the respective score a playlist database 5 ranking the plurality of sound files is determined, and the control signal is derived based on the playlist database. After choosing the playlist database 5 by pattern recognition machine learning algorithm 9 sound files are suggested 10.

Description

A METHOD AND A SYSTEM FOR CONTROLLING A CUSTOMIZED PLAYBACK OF SOUND FILES BASED ON PLAYLIST SCORING

FIELD OF THE INVENTION

[0001] The invention relates to the field of motor vehicles and playback of sound files. More particularly, the present disclosure describes a method and a system for controlling a customized playback of sound files for a user in a vehicle.

BACKGROUND INFORMATION

[0002] A typical listener does not want to hear all types of songs at all times; for instance, they might not want to hear heavy-metal music while stuck in traffic, nor would they appreciate classical orchestral music at the gym. To avoid this issue with currently available playlists, a system would require constant manual input from the user on when and what type of music they would like to hear.

[0003] It is a purpose of providers of music streaming to play music for each user automatically in a way that the played songs are chosen appropriately for the respective user. This is the difference compared to conventional music playing services like radio. A radio station plays music the same way for each listener, so that all listeners listening to a respective radio station hear the same songs at a time. In opposite to this, streaming of music allows choosing songs or sound files in a customized way for each listener or user. However, underlying requirements and possibilities for choosing songs or sound files for a customized playback in a vehicle differ a lot from streaming services used on a mobile phone for example.

[0004] The document CN 101 992 779 B discloses a method of intelligent music selection in a vehicle including learning of user preferences for music selection in the vehicle, corresponding to a plurality of driving conditions of the vehicle. However, the application primarily relates to choosing radio stations.Therefore, there exists a need in the art for a technique to enable a better customization of the playback of sound files in the context of a motor vehicle.

SUMMARY OF THE INVENTION

[0005] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages. Embodiments and aspects of the disclosure described in detail herein are considered a part of the claimed disclosure.

[0006] In one non-limiting embodiment of the present disclosure, a method for controlling a customized playback of sound files for a user in a vehicle is described. The method comprises several steps. One step consists of providing user preference data which relates to a preference of the user regarding a plurality of sound files. Another step consists of receiving environment data which relates to an environment of the vehicle and/or a ride of the vehicle and/or receiving time data which specifies a point in time for the sound files to be played. Another step comprises of deriving a control signal for controlling the playback of the sound files from at least the user preference data, the environment data and the time data. According to another step, the control signal is output over a control interface.

[0007] In one embodiment, for providing the user preference data, a respective score for a plurality of sound files is determined based on respective playback operation data for each of the plurality of sound files, wherein the playback operation data represents if and when an operational action regarding a former playback of a respective song of the plurality occurred during the former playback. It is also envisaged that based on the environment data, the time data and the respective score a playlist database ranking the plurality of sound files is determined. Further on it is envisaged that the deriving of the control signal is carried out on the basis of the playlist database. It can be appreciated that during the step of providing the user preference data the respective score for the plurality of sound files is determined. Alternatively, the determining of the respective score can be carried out prior to the providing of the user preference data. In other words, the user preference data, which consists of a plurality of respective scores for each of the sound files of the plurality, can be saved in a database after the determining of the respective score. During the step of providing the user preference data, the user preference data can then be loaded from the database.

[0008] Based on the user preference data, in particular the respective scores for the plurality of sound files, the environment data and the time data, the plurality of sound files is ranked in the playlist at a base. The playback of the sound files can then be carried out in a user-customized way on the basis of the playlist database. Therefore, the control signal for controlling the playback of the sound files is derived from the playlist database.

[0009] In another non-limiting embodiment of the present disclosure, a system for controlling a customized playback of sound files for a user in a vehicle is disclosed. The system comprises a user preference data unit configured to provide user preference data which relates to a preference of the user regarding a plurality of sound files, an environment data unit configured to receive environment data which relates to an environment of the vehicle and/or a ride of the vehicle, a time data unit configured to receive time data which specifies a point in time for the sound files to be played, a playback control unit configured to derive a control signal for controlling the playback of the sound files from at least the user preference data, the environment data and the time data, a control interface configured to output the control signal.

[0010] In one embodiment of the invention, the user preference data unit is configured to determine a respective score for the plurality of sound files based on respective playback operation data for each of the plurality of sound files, wherein the playback operation data represents if and when an operational action regarding a former playback of a respective song of the plurality occurred during the former playback. Also, the playback control unit is configured to determine a playlist database ranking the plurality of sound files based on the environment data, the time data and the respective score. Another feature is that the playback control unit is configured to carry out the deriving of the control signal on the basis of the playlist database.

[0011] This disclosure is proposing a playlist database, which can also be referred to as intuitive playlist, that accounts for user preferences automatically to choose songs appropriately without active user input. The playlist database or intuitive playlist is a method for a media player (vehicle, portable, home, etc.) to recognize contextual patterns in songs preferences with passive interpretation of the user's response to tracks played (ex. User skips track). The algorithm will then continuously refine multiple discreet playlists based on user responses and their physical location and time data. Accounting for all these conditions will result in a more accurate and intuitive playlist of songs appropriate for the user. This will create a more seamless and enjoyable user experience.

[0012] The advantage this method has over traditional playlists and other forms of smart playlist is that some playlists base recommendations from all past songs, and do not account for context of the user's current location or undertaking. Traditional smart playlists make one large playlist or force the user to make active selections of the type of playlist they want. This solution makes multiple context playlists based on physical and time-based patterns, so there is less user intervention necessary. Additionally, current smart playlists often require the user to actively select a "like" or "dislike" button to refine their suggested songs in the future, this solution will passively interpret the user's response to a track to assess their like or dislike of the song.

[0013] Further advantages, features, and details of the invention derive from the following description of preferred embodiments as well as from the drawings. The features and feature combinations previously mentioned in the description as well as the features and feature combinations mentioned in the following description of the figures and/or shown in the figures alone can be employed not only in the respectively indicated combination but also in any other combination or taken alone without leaving the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The novel features and characteristics of the disclosure are set forth in the independent claims. The accompanying drawings, which are incorporated in and constitute part of this disclosure, illustrate exemplary embodiments and together with the description, serve to explain the disclosed principles. In the figures, the same reference signs are used throughout the figures to refer to identical features and components. Some embodiments of the system and/or methods in accordance with embodiments of the present subject-matter are now described below, by way of example only, and with reference to the accompanying figures.

[0015] The drawings show in: [0016] Fig. 1 an example how to derive a score for a sound file from an operational action regarding a playback of the sound file; [0017] Fig. 2 an example how to rank a multitude of sound files within a playlist database: [0018] Fig. 3 an example reflow chart of a first embodiment of the method for controlling a customized playback of sound files for a user in a vehicle; [0019] Fig. 4 a second exemplary embodiment of a method for controlling a customized playback; and [0020] Fig. 5 a block diagram of a playback system for controlling a customized playback of sound files for a user in a vehicle.

DETAILED DESCRIPTION

[0021] In the present document, the word "exemplary' is used herein to mean "serving as an example, instance, or illustration". Any embodiment or implementation of the present subject-matter described herein as "exemplary' is not necessarily to be construed as preferred or advantageous over other embodiments.

[0022] While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0023] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus proceeded by "comprises.., a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0024] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0025] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[0026] Fig. 1 shows a graph representing a sound file 1, more particularly a music file or a song, on a time axis t representing a playback of the sound file 1. Fig. 1 also shows five lines 41-45 each representing a different operational action 7 occurring at a different time during the playback of the sound file 1. For ranking a plurality of sound files 1 the method comprises a machine learning algorithm configured to persuasively assess a user's song preferences. The machine-learning algorithm is configured to refine playlist databases, for example, each comprising one playlist of a plurality of music files 1, based on a user response to the respective palyed sound files 1. The user's response is represented by the respective operational action 7 occurring during the playback. This means the machine-learning algorithm may not require an active selection of a "like" or "dislike' button. As sound files 1, in particular songs, are played multiple times, their assigned value will be the sum total of multiple increments 8 changing the respective score of a respective sound file 1 of the plurality step by step. In doing so, the sound files 1 of the plurality will obtain higher or lower ranks in the playlist database 5. This may happen dependently from a value of the increment 8. The value of the increment 8 may for example be dependent on when the operational action 7 occurred during the playback. In particular, with respect to the time axis t, and/or which operational action 7 occurred during the playback.

[0027] As the playlist database 5 of sound files 1 expands, for example, in consequence of the additional new sound files to the playlist data base 5, the machine-learning algorithm will search for patterns in high-ranking and low-ranking songs to determine what traits to seek and what traits to avoid in new sound files 1, in particular new songs, added to the playlist database 5. In doing so, not only the sound files 1 already present in the play listed up as 5 are ranked in a more refined way but also new sound files are added on the basis of the preference of the user. Over time, the playlist is being constantly refined for the user without any of the user's active participation. It should be appreciated that for sound files 1 of the plurality ranked equal or better than a first threshold level within the playlist database 5, at least one similar sound file from an unknown plurality of sound files, whose sound files are not yet ranked in the playlist database 5, is determined. In other words, for sound files 1 with high ranking similar sound files may be added to the playlist database. Optionally, during the determination of the at least one similar sound file, the at least one similar sound file is by at least a predetermined extent different to sound files 1 of the plurality ranked equal or worse than a second threshold level. In other words, the at least one new sound file may be determined in a way that it is similar to high ranked sound files 1 from the playlist database 5 and/or different from badly ranked sound files 1.

[0028] Line 41 represents an immediate or early skip forward as the user response or operational action 7. In this case, the algorithm will interpret the operational action 7 to skip the sound file 1 forward as a negative response to the played sound file 1 or the represented song respectively. A large negative increment 8 is added to the sound files 1 over a total score 2 (fig. 2) for the current playlist database 5. If the sound file is also present in other playlist databases, the operational action 7 will not affect the other databases.

[0029] Line 42 represents a skip forward in the middle of the playback of the sound file 1 as the operational action 7. This operational action 7 is interpreted as a distinct moment where the sound file 1 or the represented song respectively was no longer desired by the user. The machine-learning algorithm may look for a distinct characteristic of the sound file 1 which could be linked to a pattern. Alternatively, an increment 8 with negative value will be added to the overall score 2 of the sound file 1. In this scenario the negative value of the increment 8 may be less than in the scenario represented in line 41.

[0030] Line 43 represents a skip forward at an end of the sound file 1 as operational action 7. This operational action 7 may be interpreted as a neutral operational action, as the sound file 1 or the song respectively could have ended or tapered into silence before the actual completion of the sound file 1. In this case, neither a positive nor a negative increment 8 may be added to the total score 2 of the sound file 1.

[0031] Line 44 represents a scenario with no operational action 7 at all. In other words, the user did not interfere the playback of the sound file 1 in this scenario. Therefore, it is assumed that the user had a positive response to the sound file 1 and the song represented by the sound file 1 was appropriately chosen. In this case, the increment 8 with a positive value is added to the total score 2 of the played sound file 1.

[0032] In the scenario represented by line 45 a skip backward or a song replay request respectively is detected as the operational action 7. If the user requests the sound file 1 to be played once more or to be replayed from the beginning at any point of the sound file 1, then an increment with a larger positive value (e.g. in comparison to the scenario of line 44) may be added to the total score 2 of the sound file 1. This is based on an interpretation of the sound file 1 or the song respectively being highly enjoyed by the user.

[0033] Now referring to Fig. 2 the ranking of the plurality of sound files 1 within the playlist database 5 is described. There may exist several playlist databases 5 parallel to each other for different conditions of environment data 3 and/or time data 4. This is described in the letter referring to fig. 4. Fig. 2 shows an exemplary structure for the plurality of sound files 1 being organized in the playlist database 5. This organization may be independent of how many playlist databases 5 coexist. To each of the sound files 1 of the plurality a respective score 2 or total score 2 is assigned. The score 2 may be derived from playback operation data on the basis of operational actions 7 regarding a former playback of a respective sound file 1 of the plurality. This may, for example, be carried out in the way like described with respect to fig. 1. The sound files 1 may be ranked according to their respective score 2, for example high to low. The higher the score of a respective song 1, the higher its rank may be.

[0034] Additionally, to a list of the plurality of sound files 1 and their respective score 2 the playlist database 5 may comprise additional information regarding the respective sound files 1. For example, the playlist database may comprise values for the following details for each sound file 1. In other words, for each of the songs 1 one or more of the following details may be assigned within the playlist database 5: general type 20 or genre 20, title 21 and/or artist 21, song length 22, top 100 position 23, tempo or speed 24 or any other detail 25.

[0035] Now referring to Fig. 3, a non-limiting and exemplary first embodiment of a method for controlling a customized playback of sound files 1 for a user in a vehicle is described by a flowchart. In a first step Si user preference data is provided by determining the respective score 2 for the plurality of sound files 1 based on the playback operation data for each of the plurality of sound files 1. The playback operation data comprises information of if and when the operational actions 7 regarding the former playback of the respective sound files 1 occurred during the former playback of the respective sound file 1. This has already been described on the basis of fig. 1.

[0036] In steps S2.1 and S2.2 environment data 3 and time data 4 is received. In particular, in the step 32.1 environment data 3 which relates to an environment of the vehicle and/or ride of the vehicle may be received. In the step 32.2 time data 4 which specifies a point in time for a respective sound file 1 to be played may be received.

[0037] In a step S2.3 the ranking of the sound files 1 of a playlist database 2 is carried out. In this step S2.3 a playlist database 5 ranking the plurality of sound files 1 is provided. The ranking itself within the playlist database 5 may be carried out like described in the context of fig. 2 mainly on the basis of the respective score 2 of the plurality of sound files 1. The environment data 3 and the time data 4 may be used for choosing a playlist database 5 amongst a plurality of playlist databases 5. Each of the plurality of playlist databases 5 is used for different conditions of the environment data 3 and/or the time data 4. In other words, depending on the point in time for sound files 1 to be played and/or the environment of the vehicle and/or the ride of the vehicle a specific playlist database 5 is chosen from the plurality of playlist databases 5. Within the environment data 3 and/or the time data 4 patterns may be determined. These patterns may indicate a dependency of the score 2 for the sound files 1 of the plurality from the environment data and/or the time data. In doing so, it is taken into account that a mood of the user may be dependent from the environment, the ride of the vehicle and/or the time of the day, the time of the year. So the patterns in the environment data 3 and the time data 4 may describe moods of the user resulting of the environment, ride and/or time. In summarization in step 33 it may be envisaged that based on the environment data 3 and/or the time data 4 a playlist database 5 from a plurality of playlist databases 5 may be chosen. Within the chosen playlist database 5 the ranking of the plurality of sound files 1 is then carried out on the basis of the respective score 2 of the sound files 1. Based on operational actions 7 the ranking may be subsequently refined. It may be envisaged that this refinement only takes place for the actual chosen playlist database 5. In other words, the other playlist databases 5 of the plurality of playlist databases 5 may not be altered while a specific playlist database 5 is chosen.

[0038] In a step S4 a control signal is derived or determined on the basis of the chosen playlist database 5. The controlled signal may indicate which of the plurality of sound files is to be played next. In other words, by the control signal it may be commanded to a playback device which of the sound files 1 are to be played and/or in which order. The control signal is output in a step S5. In the step S5 the control signal may be transmitted to the playback device. The playback device may e.g. be a multimedia system of the vehicle. The playback device may comprise at least one loudspeaker and a playback unit for playing the sound files 1. The sound files 1 may be stored within a sound file database of the playback device or be received or streamed from an external database. For example, the sound files 1 may be streamed from a streaming provider over the internet.

[0039] Fig. 4 shows a second exemplary embodiment of the method. To choose an intuitive playlist 11, the environment data 3 and/or the time data 4 is received. The environment data 3 may comprise a location of the vehicle, a GPS-signal, a value for the speed of the vehicle, information regarding the weather of the vehicle (e.g. temperature, sun radiation, cloudiness, precipitation, rainfall or snowing, or any other information regarding the weather). The time data 4 may comprise information about the time of the day, the day of the week, the season of the year or the like. In the environment data 3 and the time data 4 a pattern may be determined. Based on the environment data 3 and/or the time data 5 or the detected pattern respectively it is determined which playlist database 5 is suitable. In other words, one playlist database 5 amongst a plurality of playlist databases 5 may be chosen. Based on the patterns discovered in the environment data 3 and/or the time data 4 the separate discrete playlist databases 5 are created for each pattern to further refine which sound files 1 are appropriate for the respective pattern. With the goal of having seamless use and passive refinement of the playlist databases 5, these processes, in particular the refinement and/or choosing of the appropriate playlist database 5, may operate as background operation not viewable by the user.

[0040] After choosing the playlist database 5 by pattern recognition machine learning algorithm 9 sound files 1, in particular songs, are suggested 10. For such a suggestion 10 the user response or the operational action 7 regarding the playback of the suggested sound file may be determined. This again can be done according to the function described in fig. 1. According to the occurred operational action 7 the increment 8 can be added to the score 2 of the respective sound file 1. In doing so, the chosen playlist database 5 is further refined, as the score 2 is subsequently upgraded.

[0041] As shown in fig. 4, it can be envisaged that only the chosen playlist database 5 is affected by the increment 8. The other playlist databases 5 are not affected by the user response or the operational action 7. In doing so, the chosen playlist database 5 can be refined for a specific mood of the user indicated by the environment data 3 and the time data 4.

[0042] The suggestion 10 may comprise known songs which are already part of the chosen playlist database 5 or new songs discovered by the pattern recognition machine learning algorithm 9. Like mentioned in the context of fig. 2, new sound files 1 or songs may be suggested by an algorithm, e.g. the pattern recognition machine learning algorithm 9.

[0043] Concluding fig. 5 shows a block diagram of a playback system 30 for controlling a customized playback of sound files 1 for a user in a vehicle. The playback system 30 may be configured to perform the method for controlling a customized playback of sound files 1 for a user in a vehicle according to any of the disclosed embodiments. The playback system 30 a user preference data unit 31 configured to provide user preference data which relates to a preference of the user regarding a plurality of sound files 1, an environment data unit 32 configured to receive environment data which relates to an environment of the vehicle and/or a ride of the vehicle, a time data unit 33 configured to receive time data which specifies a point in time for the sound files to be played, a playback control unit 35 configured to derive a control signal for controlling the playback of the sound files 1 from at least the user preference data, the environment data 3 and the time data 4, and a control interface 34 configured to output the control signal.

[0044] The user preference data unit 31 is configured to determine a respective score 2 for the plurality of sound files based on respective playback operation data for each of the plurality of sound files 1, wherein the playback operation data represents if and when an operational action 7 regarding a former playback of a respective sound file 1 of the plurality occurred during the former playback. The playback control unit 35 is configured to determine a playlist database 5 ranking the plurality of sound files 1 based on the environment data 3, the time data 4 and the respective score 2, and the playback control unit 34 is configured to carry out the deriving of the control signal on the basis of the playlist database 5.

List of reference signs 1 sound file 2 score 3 environment data 4 time data playlist database 7 operational action 8 increment 9 learning algorithm suggestion 11 intuitive playlist type 21 artist 22 length 23 position 24 speed detail playback system 31 user preference data unit 32 environment data unit 33 time data unit 34 control interface playback control unit S1-S5 steps

Claims

CLAIMS1. A method for controlling a customized playback of sound files for a user in a vehicle, the method comprising the following steps: - providing user preference data which relates to a preference of the user regarding a plurality of sound files, - receiving environment data which relates to an environment of the vehicle and/or a ride of the vehicle and/or receiving time data which specifies a point in time for the sound files to be played, - deriving a control signal for controlling the playback of the sound files from at least the user preference data, the environment data and the time data, and - outputting the control signal over a control interface, characterized in that - for providing the user preference data, a respective score for a plurality of sound files is determined based on respective playback operation data for each of the plurality of sound files, wherein the playback operation data represents if and when an operational action regarding a former playback of a respective sound file of the plurality occurred during the former playback, - based on the environment data, the time data and the respective score a playlist database ranking the plurality of sound files is determined, and - the deriving of the control signal is carried out on the basis of the playlist database.
2. The method according to claim 1, characterized in that a plurality of playlist databases is determined for different conditions of the environment data and/or the time data.
3. The method according to claim 2, characterized in that for the different conditions of the environment data and/or the time data the respective score for the plurality of sound files is determined independently.
4. The method according to claim 3, characterized in that at least one pattern in a dependency of the respective score for the plurality of sound files from the different conditions of the environment data and/or the time data is determined.
5. The method according to any one of claims 1 to 4, characterized in that for each time the operational action regarding the former playback of a respective sound file of the plurality occurred during the former playback, the score of the respective sound file is altered by a predetermined increment.
6. The method according to claim 5, characterized in that the predetermined increment is dependent of when the operational action occurred during the playback.
7. The method according to claim 5 or 6, characterized in that the predetermined increment is dependent of which operational action occurred during the playback.
8. The method according to any one of claims 1 to 7, characterized in that for sound files of the plurality ranked equal or better than a first threshold level, at least one similar sound file from an unknown plurality of sound files, whose sound files are not yet ranked in the playlist database, is determined.
9. The method according to claim 8, characterized in that during the determining of the at least one similar sound file, it is envisaged, that the at least one similar sound file is different to sound files of the plurality ranked equal or worse than a second threshold level by at least a predetermined extent.
10. A playback system for controlling a customized playback of sound files for a user in a vehicle, the system comprising - a user preference data unit configured to provide user preference data which relates to a preference of the user regarding a plurality of sound files, - an environment data unit configured to receive environment data which relates to an environment of the vehicle and/or a ride of the vehicle, - a time data unit configured to receive time data which specifies a point in time for the sound files to be played, - a playback control unit configured to derive a control signal for controlling the playback of the sound files from at least the user preference data, the environment data and the time data, and - a control interface configured to output the control signal, characterized in that - the user preference data unit is configured to determine a respective score for the plurality of sound files based on respective playback operation data for each of the plurality of sound files, wherein the playback operation data represents if and when an operational action regarding a former playback of a respective sound file of the plurality occurred during the former playback, - the playback control unit is configured to determine a playlist database ranking the plurality of sound files based on the environment data, the time data and the respective score, and - the playback control unit is configured to carry out the deriving of the control signal on the basis of the playlist database.