PERSONAL AUDIO SYSTEM USING PROCESSING PARAMETERS LEARNED FROM USER FEEDBACK

• US 20160142820A1
• Filed: 01/28/2016
• Published: 05/19/2016
• Est. Priority Date: 04/08/2014
• Status: Active Grant

• Current Assignee: Dolby Laboratories Licensing Corporation (Dolby Laboratories Incorporated)
• Original Assignee: Doppler Labs, Inc.
• Inventors: Wiggins, Daniel, Kraft, Noah, Lanman, Richard Fritz III, Baker, Jeff
• Granted Patent: US 9,736,264 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/165 Management of the audio stream, e.g. setting of volume, audio stream path
• CPC Class Codes: H04L 67/04 specially adapted for terminals or networks with limited capabilities; specially adapted for terminal portability
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 69/04 Protocols for data compression, e.g. ROHC
• CPC Class Codes: H04L 69/18 Multiprotocol handlers, e.g. single devices capable of handling multiple protocols
• CPC Class Codes: H04R 1/1083 Reduction of ambient noise active noise reduction per se G10K11/175; protective devices for the ear, e.g. providing acoustic protection A61F11/06
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/50 Service provisioning or reconfiguring
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Learning personal audio systems and methods are disclosed. A learning personal audio system characterizes the digitized ambient sound so as to generate feature data for the digitized ambient sound, instructs a network interface to transmit the feature data to the remote server, requests one or more appropriate sound profiles from the remote server based upon the feature data, receives one or more selected sound profiles, selected by the remote server based upon the feature data, and initiates processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound. The one or more selected sound profiles for the learning personal audio system are based upon sound profiles which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.

Claims

1. A learning personal audio system, comprising:

a housing configured to interface with a user'"'"'s ear;

an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound;

a network interface for receiving one or more sound profiles each with a respective sets of processing parameters from a remote server;

a memory for storing one or more of the sound profiles received using the network interface; and

a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to

characterize the digitized ambient sound so as to generate feature data for the digitized ambient sound,

instruct the network interface to transmit the feature data to the remote server,

request one or more appropriate sound profiles from the remote server based upon the feature data,

receive one or more selected sound profiles, selected by the remote server based upon the feature data, and

initiate processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound; and

an output subsystem disposed within the housing and in communication with the processor, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another, the output subsystem configured to convert the digitized processed sound into processed output sound for the user'"'"'s ear;

wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.

2. The learning personal audio system of claim 1 further comprising:

a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system;

wherein the feature data further includes the current location; and

the one or more selected sound profiles are also based upon the current location of the learning personal audio system.

3. The learning personal audio system of claim 2 wherein the current location is used to select the one or more selected sound profiles based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.

4. The learning personal audio system of claim 2 wherein the one or more selected sound profiles are selected based upon location type, as defined by a categorization made by the remote server comparing a location type of the current location to a database of potential location types, and based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.

5. The learning personal audio system of claim 4 wherein the location type is one selected from the group comprising a stadium, a concert hall, a basketball court, an outdoor concert venue, a restaurant, an auditorium, an amphitheater, a classroom, a running trail, and an athletic field.

6. The learning personal audio system of claim 1 wherein the one or more selected sound profiles are also selected using a context.

7. The learning personal audio system of claim 1 wherein the one or more sound profiles are selected based upon a median one or more sound profiles selected by the plurality of other users.

8. The learning personal audio system of claim 1 wherein the one or more sound profiles are selected based upon an average one or more sound profiles selected by the plurality of other users.

9. A method for processing personal audio based using forward feedback, comprising:

converting ambient sound into digitized ambient sound using an input subsystem disposed within a housing, the input subsystem comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another;

receiving, via a network interface, one or more sound profiles each with a respective sets of processing parameters from a remote server;

characterizing the digitized ambient sound so as to generate a feature datafor the digitized ambient sound;

instructing the network interface to transmit the feature data to the remote server;

requesting one or more appropriate sound profiles from the remote server based upon the feature data;

receiving one or more selected sound profiles, selected by the remote server based upon the feature data;

storing, in a memory, one or more of the sound profiles received using the network interface;

initiating processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound;

converting the digitized processed sound into processed output sound for the user'"'"'s ear using an output subsystem disposed within the housing, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another; and

wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.

10. The method of claim 9 further comprising:

determining a current location for the learning personal audio system using a a geo-location engine;

wherein the feature data further includes the current location; and

the one or more selected sound profiles are also based upon the current location of the learning personal audio system.

11. The method of claim 10 wherein the current location is used to select the one or more selected sound profiles based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.

12. The method of claim 10 wherein the one or more selected sound profiles are selected based upon location type, as defined by a categorization made by the remote server comparing a location type of the current location to a database of potential location types, and based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.

13. The method of claim 12 wherein the location type is one selected from the group comprising a stadium, a concert hall, a basketball court, an outdoor concert venue, a restaurant, an auditorium, an amphitheater, a classroom, a running trail, and an athletic field.

14. The method of claim 9 wherein the one or more selected sound profiles are also selected using a context.

15. The method of claim 9 wherein the one or more sound profiles are selected based upon a median one or more sound profiles selected by the plurality of other users.

16. The method of claim 9 wherein the one or more sound profiles are selected based upon an average one or more sound profiles selected by the plurality of other users.

17. A learning personal audio system, comprising:

a housing configured to interface with a user'"'"'s ear;

an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound;

a network interface for transmitting feature data for use in generating one or more sound profiles each with a respective sets of processing parameters by a remote server;

a memory for storing digitized ambient sound and feature data; and

a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to

characterize the digitized ambient sound so as to generate feature data for the digitized ambient sound,

capture user manual selection data comprising data identifying the selection of at least one set of processing parameters associated with the digitized ambient sound,

instruct the network interface to transmit the feature data and selection data to the remote server for use by the remote server in identifying approporiate sound profiles for use by personal audio systems in processing sound with features similar to those identified by the feature data.

18. The learning personal audio system of claim 17 further comprising:

a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system; and

wherein the network interface is further instructed to transmit the current location along with the feature data and the selection data.

19. A learning personal audio system, comprising:

a housing configured to interface with a user'"'"'s ear;

an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound;

a network interface for receiving one or more sound profiles each with a respective sets of processing parameters from a remote server;

a memory for storing one or more of the sound profiles received using the network interface; and

a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to

request one or more appropriate sound profiles from the remote server based upon feature data of the digitized ambient sound,

receive one or more selected sound profiles, selected by the remote server based upon the feature data, and

initiate processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound; and

an output subsystem disposed within the housing and coupled to the processor, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another, the output subsystem configured to convert the digitized processed sound into processed output sound for the user'"'"'s ear;

wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.

20. The learning personal audio system of claim 19 further comprising:

a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system; and

wherein the network interface is further instructed to transmit the current location along with the feature data.