Hotword Cleaner: Dual-microphone Adaptive Noise Cancellation with Deferred Filter Coefficients for Robust Keyword Spotting

Huang, Yiteng; Shabestary, Turaj Zakizadeh; Gruenstein, Alexander

doi:10.1109/icassp.2019.8682682

Cited by 27 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, a more popular [61], [93], [94], [122] and simpler AGC method called PCEN (Per-Channel Energy Normalization) [156] was proposed for KWS. Keeping the original notation of [156], E(t, f ) represents (Mel) filterbank energy at time frame t and frequency bin f , and…”

Section: ) Gain Control For Far-field Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Spoken Keyword Spotting: An Overview

López-Espejo¹,

Hansen²,

Jensen³

2022

IEEE Access

View full text Add to dashboard Cite

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS.INDEX TERMS Keyword spotting, deep learning, acoustic model, small footprint, robustness.

show abstract

Section: ) Gain Control For Far-field Conditionsmentioning

confidence: 99%

“…Presumably thinking of voice assistant use cases, Google developed a series of noise-robust KWS methods based on dual-microphone adaptive noise cancellation (ANC) to particularly deal with speech interference [94], [122], [162]. The working principle of ANC is outlined in Figure 8.…”

Section: ) Adaptive Noise Cancellationmentioning

confidence: 99%

Deep Spoken Keyword Spotting: An Overview

López-Espejo¹,

Hansen²,

Jensen³

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When a device has multiple commands that can accomplish similar or different tasks, there can be confusion for users to decide which command to use, which is why the Information Systems Department from the University of Maryland, Baltimore County (UMBC) has researched this topic [3]. There has also been tests of improving hotword detection using an accelerometer sensor [8]. Using the sensor and its data that is found on many current mobile devices, the accuracy of detecting hotwords have increased while also lowering energy costs.…”

Section: Related Workmentioning

confidence: 99%

“…One research group from Wilbur Wright College experimented with a mobile reading device for people that are blind, and another team created a Sudoku game for blind people as well [6], [7]. Other ideas for improvements to the overall experience of hotword detection and personal assistants involve implementing accelerometer sensors and its data to detect hotwords in order to lower energy costs and increase the assistants hotword detection accuracy [8]. To improve hotword detection in loud or in public area, Google has also tried to use dual microphones in order to filter out the ambient noise and to find just the hotword the user said [9].…”

Section: Introductionmentioning

confidence: 99%

Experimenting with Hotword Detection: The Pao-Pal

Yang¹,

Jee²,

Leblanc³

et al. 2020

EJECE

View full text Add to dashboard Cite

Hotword detection and voice command are implemented in many of today’s current technological devices. In this paper, the implementations using hotword detection as well as the improvements in hotword technology will be discussed including the Pao-Pal. The Pao-Pal was made using Google’s AIY Voice Kit which contains a Raspberry Pi Zero WH, voice bonnet, and a speaker that allows it to playback pre-recorded voice lines. These components allow the Pao-Pal to act as a switch for a Keurig machine to make coffee when certain phrases are said. To allow the Pao-Pal to always listen for hotwords, it uses a hotword detection engine called Snowboy, which also allows it to playback the pre-recorded voice lines. Snowboy allows users to create and customize their own special hotword. Other projects concerning voice commands will be discussed along with the application of that kind of technology.

show abstract

“…An acoustic echo cancellation (AEC) system is generally required to perform voice control on music playback devices, i.e., smart speakers, where the coupling of closely spaced loudspeakers and microphones can create very challenging signal-to-echo ratios [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Robust STFT Domain Multi-Channel Acoustic Echo Cancellation with Adaptive Decorrelation of the Reference Signals

Bagheri¹,

Giacobello²

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

In this paper, we present an algorithm for multi-channel acoustic echo cancellation for a high-fidelity audio reproduction system equipped with a microphone array for voice control. Since a key requirement for this type of systems is leaving the reference signals that drive the multiple loudspeakers unaltered, we propose an adaptive decorrelation procedure in the time domain before feeding these signals to the echo cancellers. This helps mitigate the so-called non-uniqueness problem arising from their high correlation. This approach offers several advantages: it is extendable to a varying number of channels, requires low computational complexity, and preserves the original reference content. The echo cancellation approach applied to the decorrelated reference signals is then based on STFT-domain robust adaptive methods that do not require doubletalk detection. The combination of these two techniques makes our approach particularly desirable for multi-channel echo cancellation problems with very low signal-to-echo ratio.

show abstract

Hotword Cleaner: Dual-microphone Adaptive Noise Cancellation with Deferred Filter Coefficients for Robust Keyword Spotting

Cited by 27 publications

References 16 publications

Deep Spoken Keyword Spotting: An Overview

Deep Spoken Keyword Spotting: An Overview

Experimenting with Hotword Detection: The Pao-Pal

Robust STFT Domain Multi-Channel Acoustic Echo Cancellation with Adaptive Decorrelation of the Reference Signals

Contact Info

Product

Resources

About