Amazon Technologies, Inc.

Sumit Garg

Ameya Agaskar

Christopher Evans

Zhengping Jin

Mohammad Edris Qarghah

James G Droppo

Chao Wang

Ming Sun

Chieh-Chi Kao

Qingming Tang

Ahmed Abdelal

Rong Chen

Chia-Jung Chang

Nagaraj Mahajan

Qin Zhang

Sai Kiran Venkata Subramanya Rupanagudi

Aaron Challenner

Henry Michael D Souza

Brendon Jude Wilson

Terrence Yu

David McGuire

Siddharth Agrawal

Pavas Kant

Nathaniel White

Yuxuan Hao

Ravina Chopra

Cindy Angie Hou

Described herein is a system for performing watermark detection using multiple time shifts to increase a resolution of watermark detection. Instead of decoding blocks of successive audio frames (e.g., 10 ms of audio) using a single watermark decoder, watermark verification can be performed by decoding overlapping frame shifts using multiple decoders in parallel, thereby increasing a chance that one of the watermark decoders will be synchronized with the embedded audio watermark. For example, watermark verification may split audio data into parallel streams and decode using two decoders (e.g., 2× shifts-per-frame), four decoders (e.g., 4× shifts-per-frame), or the like. Increasing resolution by performing overlapping detection increases an accuracy of the watermark detection without changing the embedded audio watermark.

Decoding audio watermarks using time shifts

Described herein is a system for encoding audio watermarks with time extensions to enable enhanced watermark detection. An extended audio watermark may include an existing audio watermark and a watermark extension, enabling backwards compatibility with existing watermark detection while also enabling enhanced watermark detection with increased accuracy. For example, embedding the extended acoustic watermark enables (i) limited devices to perform watermark detection to detect the existing audio watermark, and (ii) improved devices to perform enhanced watermark detection to detect the extended audio watermark. As the extended audio watermark has a longer time duration than the existing audio watermark, an accuracy of performing enhanced watermark detection is increased relative to detecting the existing audio watermark alone.

Extended audio watermarks

A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Acoustic event detection

Described herein is a system for encoding audio watermarks with frequency extensions to enable enhanced watermark detection. An extended audio watermark may include an existing audio watermark and a duplicate audio watermark, enabling backwards compatibility with existing watermark detection while also enabling enhanced watermark detection with increased accuracy. For example, embedding the extended audio watermark enables (i) limited devices to perform watermark detection to detect the existing audio watermark, and (ii) improved devices to perform enhanced watermark detection to detect the extended audio watermark. As the extended audio watermark includes redundancy in the form of duplicate audio watermark(s), an accuracy of performing enhanced watermark detection is increased relative to detecting the existing audio watermark alone.

A system configured to perform content recognition using fingerprinting to recognize known media content. A device determines fingerprints based on decoded content data to be sent using a media interface component to an output component. Metadata related to the content/device/fingerprint may also be created. The fingerprints and metadata are sent by the device to a supporting system for orchestration and matching of the fingerprints to known media content.

Content recognition using fingerprinting

A system configured to perform content identification using fingerprinting to recognize known media content. The system may generate a reference database including reference fingerprints for each media content item to include in the content identification. In addition, the system may generate a hash table that associates individual frames of the reference fingerprints with identification information for corresponding media content items. When a device is playing media content, the system may perform content identification by generating query fingerprints representing the media content and comparing the query fingerprints to the reference database. For example, the system may match a query fingerprint to a reference fingerprint by identifying which of the reference fingerprints shares the most frames with the query fingerprint using the hash table. In addition, the system may use additional decision criteria to confirm a match, such as fine-grain matching or tracking successive fingerprints over time.

Content identification using fingerprinting

Described herein is a system for embedding audio watermarks. To improve performance without a user perceiving the audio watermark, a system embeds audio watermarks in audio data using scaling factors that are calculated based on a spectral masking level for each frame of the audio data. The scaling factors may vary over time and correspond to an amplitude of the audio watermark across a series of watermark frames. The system processes the audio data to determine a spectral mask, which represents an amount of energy perceived in a first frequency range that is caused by energy represented in neighboring frequency ranges. By selecting scaling factor values that keep an amplitude of the audio watermark below the threshold indicated by the spectral mask, the system may embed the audio watermark in the first audio data without the audio watermark being audible to the user.

Audio watermarking

Techniques for performing audio-based device location determinations are described. A system may send, to a first device, a command to output audio requesting a location of the first device be determined. A second device may receive the audio and send, to the system, data representing the second device received the audio, where the received data includes spectral energy data representing a spectral energy of the audio as received by the second device. The system may, using the spectral energy data, determine attenuation data representing an attenuation experienced by the audio as it traveled from the first device to the second device. The system may generate, based on the attenuation data, spatial relationship data representing a spatial relationship between the first device and the second device, where the spatial relationship data is usable to determine a device for outputting a response to a subsequently received user input.

Audio-based device locationing

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Acton, MA, United States of America

Sumit Garg