# US 9,883,305 B2 · Non-linear control of loudspeakers > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,883,305 B2 - Document ID: US9883305B2 - Status: Granted - Kind: B2 - Application number: 15/125,877 - Filed: 2015-03-19 - Published: 2017-01-05 - Granted: 2018-01-30 - Earliest claimed: 2014-03-19 - Assignees: Cirrus Logic, Inc. - Applicants: Cirrus Logic International Semiconductor Ltd. - Inventors: Par Gunnars Risberg; Ylva Bjork; Ebba Wilhelmsson; Landy Toth - Web page: https://patentgrep.com/patent/US9883305B2 ## CPC - H04R 29/003: {of the moving-coil type} - H04R 3/002: {Damping circuit arrangements for transducers, e.g. motional feedback circuits} - H04R 3/007: {Protection circuits for transducers} - H04R 3/04: for correcting frequency response - H04R 29/001: {for loudspeakers (H04R29/007 takes precedence)} - H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K) ## Abstract A nonlinear control system includes a controller, a model updater, and a model. The controller is configured to accept one or more input signals, and one or more updates generated by the model updater to produce one or more control signals. The system is configured to drive one or more transducers with the control signals to produce a rendered audio stream therefrom. The model updater is configured to analysis one or more portions of the audio stream and to update one or more aspects of the controller so as to alter performance of the transducer. ## Claims ### Claim 1 A nonlinear control system for rendering a media stream through a transducer, the nonlinear control system comprising: a controller comprising a model configured to accept an input signal relating to the media stream and to output a control signal to drive an amplifier and/or the transducer for rendering the media stream thereupon, the model configured to compensate for one or more acoustic characteristics of the transducer, the amplifier, and/or an environment; one or more sensors coupled with the transducer, the amplifier, and/or the environment and configured to generate a feedback signal therefrom; and a model updater coupled with the controller, configured to accept a dataset derived from the feedback signal, the input signal, the control signal, and/or a signal generated therefrom, and to update one or more aspects of the model based upon an analysis of the dataset; wherein the model updater comprises or interfaces with a bank of models, each model in the bank configured to generate an estimate of a state from the dataset, the model updater configured to compare the state against one or more aspects of the dataset as part of the analysis. ### Claim 2 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the one or more of sensors are configured to measure or generate a signal related to a current, voltage, an impedance, a conductance, a substantially DC impedance value, a resonant property, a temperature, a voice coil current, a voice coil temperature, a membrane or coil displacement, a velocity, an acceleration, an air flow, a chamber back pressure, a transducer vent airflow, a sound pressure level, a kinetic measurement, a magnetic field measurement, a pressure, a humidity, or a combination thereof. ### Claim 3 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the controller is configured to operate at a rendering rate and the model updater is configured to periodically update the model at an updating rate, the updating rate substantially slower than the rendering rate. ### Claim 4 (depends on 3) The nonlinear control system in accordance with claim 3, wherein the updating rate is less than 1 update per hour. ### Claim 5 (depends on 3) The nonlinear control system in accordance with claim 3, further comprising a scheduler configured to determine the updating rate through analysis of the dataset. ### Claim 6 (depends on 5) The nonlinear control system in accordance with claim 5, wherein the scheduler is configured to analyze one or more metrics associated with the dataset to determine a subset thereof that is suitable for performing an update therefrom. ### Claim 7 (depends on 6) The nonlinear control system in accordance with claim 6, wherein metrics are associated with amplitude, bandwidth, relationships between, or combinations thereof in the input signal, control signal, rendered media stream, and/or feedback signal. ### Claim 8 (depends on 1) The nonlinear control system in accordance with claim 1, further comprising a buffer coupled with the model updater and configured to store at least a portion of the dataset. ### Claim 9 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the model updater comprises a robust regression algorithm to perform at least a portion of the analysis. ### Claim 10 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the model updating function comprises a selection algorithm, the selection algorithm configured to select a model from a model bank or a model related to a model in the model bank based upon the comparison. ### Claim 11 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the system is configured to accept a notification, the notification integrated into the media stream, at least a portion of the dataset derived from the media stream rendered during the notification. ### Claim 12 (depends on 11) The nonlinear control system in accordance with claim 11, wherein the notification comprises a media clip relating to a ring tone, a wakeup notification, a game sound clip, a media introduction, a video clip, movie or TV show clip, a song clip, an event, a power-up event, a user notification, a sleep recovery event, a touch audio response, or a combination thereof associated with the rendered media stream. ### Claim 13 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the model performs a change detection algorithm configured to analyze the dataset to determine if a substantial difference exists between the model in the controller and one or more acoustic characteristics of the transducer. ### Claim 14 (depends on 13) The nonlinear control system in accordance with claim 13, wherein the model in the controller comprises a linear dynamic model and a nonlinear model. ### Claim 15 (depends on 14) The nonlinear control system in accordance with claim 14, wherein the model updater is configured to update a portion of the linear dynamic model or the nonlinear model based upon the analysis of the dataset. ### Claim 16 (depends on 13) The nonlinear control system in accordance with claim 13, wherein the change detection algorithm is used to determine at least a portion of an updating rate. ### Claim 17 (depends on 13) The nonlinear control system in accordance with claim 13, wherein the transducer includes a deficient acoustic characteristic of sufficient severity so as to corrupt rendering of the input signal without compensation, the model in the controller configured to compensate for the deficient acoustic characteristic so as to effectively render the media stream on the transducer without substantial corruption. ### Claim 18 (depends on 17) The nonlinear control system in accordance with claim 17, wherein the transducer is a speaker, and the deficient acoustic characteristic is a nonlinearity and/or instability of a force factor, stiffness, and/or mechanical resistance associated with the speaker. ### Claim 19 (depends on 17) The nonlinear control system in accordance with claim 17, wherein uncompensated deficient acoustic characteristic contributes to more than 10% of an acoustic output from the transducer, the model in the controller configured to reduce this contribution by less than 10%. ### Claim 20 (depends on 17) The nonlinear control system in accordance with claim 17, wherein the model updater is configured to update the model in the controller whenever the compensated deficient acoustic characteristic contributes more than 5% above a residual threshold thereof. ### Claim 21 (depends on 17) The nonlinear control system in accordance with claim 17, wherein the transducer is designed to have a relatively high efficiency while sacrificing sound quality, THD, and/or IMD in an uncompensated operating state, the controller configured to substantially improve the sound quality, THD and/or IMD while maintaining the relatively high efficiency thereof in a compensated operating state. ### Claim 22 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the nonlinear control system is included in a mobile consumer electronic device. ### Claim 23 (depends on 22) The nonlinear control system in accordance with claim 22, wherein the consumer electronics device is a smartphone, a tablet computer, a wearable computing device, or a soundbar. ### Claim 24 (depends on 1) The nonlinear control system in accordance with claim 1, wherein the amplifier, the scheduler, and/or the model updater comprises a means for estimating a characteristic temperature of the transducer from one or more of the feedback signals, and delivering the estimate to one or more of the controller and/or the model updater, the controller and/or the model updater configured to incorporate the temperature estimate into the compensation and/or analysis algorithms, respectively. ### Claim 25 (depends on 1) Use of a system in accordance with claim 1, to improve the efficiency of a transducer family without substantially compromising sound quality. ### Claim 26 (depends on 1) Use of a system in accordance with claim 1, to reduce THD and/or IMD in a rendered media stream. ### Claim 27 A method for updating a model for use in rendering an audio stream on a transducer comprising: collecting data associated with the audio stream over one or more time periods to form a dataset; analyzing the dataset to determine if content has amplitude and spectral content above a predetermined threshold sufficient to perform the update; generating an updated model or portion thereof using at least a portion of the dataset; updating the model with the updated model or portion thereof; and comparing output of a plurality of predetermined models against at least a portion of the dataset, and selecting a model associated with one of the plurality of predetermined models to be the updated model, wherein the comparison is based upon analysis of a metric comparing closeness of fit between the predetermined models and the portion of the dataset. ### Claim 28 (depends on 27) The method in accordance with claim 27, wherein metric is a robust residual, a cumulative sum of error, a maximum likelihood assessment, a likelihood ratio test, a square residual threshold test, an amplitude comparison between input and output across frequency bands of interest, or a combination thereof between one or more estimates generated by predetermined models and the dataset. ### Claim 29 (depends on 27) The method in accordance with any one of claim 27, wherein at least one of the one or more time periods is longer than 0.1 second.