IP Library › Granted Patent US 12,730,606
Granted Patent B2
US 12,730,606 · App. 18/825,837 · Granted Sep 8, 2026

Noise classification for event detection

Inventors: Nick D'Amato (Santa Barbara, CA); Kurt Thomas Soto (Ventura, CA); Connor Kristopher Smith (New Hudson, MI)
Assignee: Sonos, Inc.
G06F3/167G06F3/162G06F3/165G10L15/22H04L12/2809H04R3/12H04R2227/005
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Quick Facts
Patent No.
US 12,730,606
App. No.
18/825,837
Granted
Sep 8, 2026
Kind
B2
Abstract

In one aspect, a network microphone device includes a plurality of microphones and is configured to detect sound via the one or more microphones. The network microphone device may capture sound data based on the detected sound in a first buffer, and capture metadata associated with the detected sound in a second buffer. The network microphone device may classify one or more noises in the detected sound and cause the network microphone device to perform an action based on the classification of the respective one or more noises.

Claims (63)

1 . A network microphone device comprising:

one or more processors;

one or more microphones; and

data storage storing instructions executable by the one or more processors to cause the network microphone device to perform operations comprising:

detecting sound via the one or more microphones;

capturing metadata associated with the detected sound;

determining a set of likely noise classifications based on the captured metadata;

determining a zone location associated with the network microphone device;

selecting a noise classification from the set of likely noise classifications based at least in part on the determined zone location; and

performing an action based on the selected noise classification.

2 . The network microphone device of claim 1 , wherein determining the set of likely noise classifications comprises:

processing the metadata to generate a probability distribution representing likelihoods of multiple predetermined noise classifications; and

identifying noise classifications having a likelihood above a threshold value.

3 . The network microphone device of claim 1 , wherein determining the zone location comprises:

identifying one or more additional network devices that are grouped with the network microphone device; and

determining a zone name associated with the grouped network devices.

4 . The network microphone device of claim 1 , wherein selecting the noise classification comprises:

identifying noise sources commonly associated with the determined zone location; and

weighting the set of likely noise classifications based on the commonly associated noise sources.

5 . The network microphone device of claim 1 , wherein performing the action comprises transmitting, to a remote computing device, (i) an indication of the selected noise classification and (ii) an indication of the determined zone location.

6 . The network microphone device of claim 1 , wherein determining the set of likely noise classifications comprises projecting frequency response data from the captured metadata onto an eigenspace corresponding to aggregated frequency response spectra from a population of network microphone devices.

7 . The network microphone device of claim 1 , wherein the operations further comprise:

determining a sound pressure level of the detected sound; and

wherein selecting the noise classification is additionally based on whether the determined sound pressure level exceeds an expected level for the determined zone location.

8 . A method comprising:

detecting sound via one or more microphones of a network microphone device;

capturing metadata associated with the detected sound;

determining a set of likely noise classifications based on the captured metadata;

determining a zone location associated with the network microphone device;

selecting a noise classification from the set of likely noise classifications based at least in part on the determined zone location; and

performing an action based on the selected noise classification.

9 . The method of claim 8 , wherein determining the set of likely noise classifications comprises:

processing the metadata to generate a probability distribution representing likelihoods of multiple predetermined noise classifications; and

identifying noise classifications having a likelihood above a threshold value.

10 . The method of claim 8 , wherein determining the zone location comprises:

identifying one or more additional network devices that are grouped with the network microphone device; and

determining a zone name associated with the grouped network devices.

11 . The method of claim 8 , wherein selecting the noise classification comprises:

identifying noise sources commonly associated with the determined zone location; and

weighting the set of likely noise classifications based on the commonly associated noise sources.

12 . The method of claim 8 , wherein performing the action comprises transmitting, to a remote computing device, (i) an indication of the selected noise classification and (ii) an indication of the determined zone location.

13 . The method of claim 8 , wherein determining the set of likely noise classifications comprises projecting frequency response data from the captured metadata onto an eigenspace corresponding to aggregated frequency response spectra from a population of network microphone devices.

14 . The method of claim 8 , further comprising:

determining a sound pressure level of the detected sound; and

wherein selecting the noise classification is additionally based on whether the determined sound pressure level exceeds an expected level for the determined zone location.

15 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors of a network microphone device, cause the network microphone device to perform operations comprising:

detecting sound via one or more microphones of the network microphone device;

capturing metadata associated with the detected sound;

determining a set of likely noise classifications based on the captured metadata;

determining a zone location associated with the network microphone device;

selecting a noise classification from the set of likely noise classifications based at least in part on the determined zone location; and

performing an action based on the selected noise classification.

16 . The one or more non-transitory computer-readable media of claim 15 , wherein determining the set of likely noise classifications comprises:

processing the metadata to generate a probability distribution representing likelihoods of multiple predetermined noise classifications; and

identifying noise classifications having a likelihood above a threshold value.

17 . The one or more non-transitory computer-readable media of claim 15 , wherein determining the zone location comprises:

identifying one or more additional network devices that are grouped with the network microphone device; and

determining a zone name associated with the grouped network devices.

18 . The one or more non-transitory computer-readable media of claim 15 , wherein selecting the noise classification comprises:

identifying noise sources commonly associated with the determined zone location; and

weighting the set of likely noise classifications based on the commonly associated noise sources.

19 . The one or more non-transitory computer-readable media of claim 15 , wherein performing the action comprises transmitting, to a remote computing device, (i) an indication of the selected noise classification and (ii) an indication of the determined zone location.

20 . The one or more non-transitory computer-readable media of claim 15 , wherein determining the set of likely noise classifications comprises projecting frequency response data from the captured metadata onto an eigenspace corresponding to aggregated frequency response spectra from a population of network microphone devices.

Assignments (2)
SECURITY INTEREST Recorded Jan 30, 2026
From: SONOS, INC.
To: JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT
Reel/Frame 074533/0615 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 5, 2024
From: D'AMATO, NICK; SOTO, KURT THOMAS; SMITH, CONNOR KRISTOPHER
To: SONOS, INC.
Reel/Frame 068501/0806 →
Continuity (5)
Continuation 18331580 · Jun 8, 2023
Continuation 17662302 · May 6, 2022
Continuation 17247736 · Dec 21, 2020
Continuation 16528016 · Jul 31, 2019
Related Publication 20250068388A1 · Feb 27, 2025
References Cited (9)
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