In open offices, overheard conversations are often cited as the main source of distraction: disregarding human speech is a very difficult task for the human brain, especially when speech is intelligible. One way of reducing the annoyances of intelligible speech is to mask speech using natural noises such as our Rain and Stream Noises, or this babble noise, as further explained.
Babble noise can be used in an office for privacy reasons as well, when calls or conversations need to remain confidential. Private offices often appear to provide privacy but often do not perform well in terms of acoustics. Sound masking between adjacent offices can be used to ensure that confidential conversations remain confidential. Babble noise is considered as one of the best noises for masking speech. Compared to White Noise, babble noise offers a higher efficiency when it comes to camouflaging voice. This means that lower masking levels can be used while ensuring the same privacy.
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When the frequency characteristics of the masking noise differ too much from the noise you are willing to camouflage, higher masking levels will be needed to cover the nuisance. When the masking levels becomes too loud for a comfortable use, we suggest trying a strategy inspired from a well know proverb: if you can't beat your enemies, join them!
In our case, this strategy consists of using one babble noise to mask another. Of course, the masking babble noise must be carefully designed to offer a better alternative to the original nuisance. Instead of using a simple babble recording, we use re-synthesis to create an artificial babble noise that sounds totally unintelligible while still sounding human. By sharing the same acoustic properties as the nuisance, our masking signal now achieves higher efficiency and can be used at quieter levels than any other masking sound. And because of the particular nature of our masking noise, even when the nuisance spikes above the mask, it will still be camouflaged.
Try listening to our babble noise for a quarter of an hour, and see if your brain slowly learns how to filter it out from your conscious perception. In most cases, it will, after a couple of minutes. Then, you will realize that not only has the masking noise been wiped from your perception, but the unwanted noise has as well.
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Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise.
A productivity tool in the form of live voice recordings. Babble noise is proven to boost productivity and creativity and help eliminate distractions by adding waves of ambient sound and hiding the distracting noises.
Designed by a former Roland PHD Sound Engineer, babble noise is proven to offer a higher level of efficiency when camouflaging speech effectively ensuring the same amount of privacy using lower masking levels.
Sound masking is an effective productivity tool that also offers speech privacy in the form of sound. Ambient noise is proven to protect private conversations and boost productivity, as well as creativity, by helping to reduce unwanted noises, eliminate distractions and prevent outsiders from overhearing sensitive information. Corporate offices, doctor offices, therapists' offices, law offices, hospitals, pharmacies, hotel lobbies, church offices, and generally open office floor-plans often benefit from sound masking devices. The added noise covers existing sounds in the area making the atmosphere more acoustically comfortable and overall providing a more pleasant and private place for employees, customers or patients.
Background: Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. This study addresses the clinical problem of measuring speech intelligibility in children with velopharyngeal insufficiency (VPI) in real-world conditions.
Aims: The purpose of the study was to investigate the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) compared to typical speech. The study further determined the contribution of nasalance and articulation accuracy in judgments of intelligibility.
Methods & procedures: Fifteen speakers diagnosed with VPI and their typical peers provided audio recordings of 20 sentences from the Hearing in Noise Test. Speech samples were presented over quiet and noise (+5 dB signal-to-noise ratio) conditions to 70 nave listeners. Intelligibility scores from nave listeners' orthographic transcriptions were obtained as the percentage of correctly identified words.
Conclusions & implications: The current work suggests that background noise will significantly affect reductions in intelligibility in both groups; the effect is more prominent in VPI speech. It was also further noted that articulation accuracy significantly affected intelligibility in quiet and noise rather than nasalance scores.
What this paper adds: What is already known on the subject Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. Accordingly, it is essential to determine the degree to which speech assessments in the clinic can predict communication difficulties in the presence of background noise in real life. Background noise can adversely cause speech intelligibility degradation in individuals with speech disorders. What this study adds The study examined the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) secondary to cleft palate compared to typical speech. The study results suggested that the presence of background noise will significantly affect reductions in intelligibility in both groups; however, the effect is more prominent in VPI speech. What are the clinical implications of this work? We found out that the intelligibility of VPI speech is lower in the presence of background noise, and therefore, assessments of speech intelligibility in clinical settings should take this into account. To ensure effective communication in noisy environments, recommended strategies include selecting quiet locations, eliminating potential distractions and supplementing communication with nonverbal cues. It is important to recognize that the effectiveness of these strategies may vary depending on the individual and the specific communication context.
Cherry coined the term cocktail party problem in the 1950s and studied how humans understand what one person is saying when others are speaking simultaneously. In a real-world environment, such as a cocktail party, competing voices, like other noises, originate from different locations in space. Thus, each ear receives signals with different intensities and waveforms. We also benefit from the head shadow effect. Our left ear hears the sounds that come from the left better than the right ear. For starters, single-channel noise suppression engines do not benefit from binaural hearing or visual cues like humans.
The number of speakers in the babble and the similarity of speech to the desired target speech make the cocktail party problem challenging for machines to tackle.The characteristics of the babble noise constantly change as people carry on conversations. Imagine a cocktail party, people close to you stop talking to take a sip, and a waiter walks around the room. Thus, the noise to suppress constantly changes. Noise suppression engines using traditional signal processing techniques do not perform well in the cases of changing (nonstationary) noises because they look for long-term patterns. Naturally, there are no stable patterns in changing noises. Advances in machine learning in the last few years have proved that deep neural network architectures successfully remove both stationary and nonstationary noises, outperforming the traditional DSP methods significantly. However, not every deep neural network architecture performs equally. Listen to how Koala Noise Suppression and Mozilla RNNoise perform in the presence of babble noise.
Copyright: 2021 Toscano, Toscano. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
One novel context is understanding speech produced while wearing a face mask. As a result of the COVID-19 pandemic, public health officials have recommended that individuals wear masks to help reduce the spread of the SARS-CoV-2 virus [5]. Masks are effective in decreasing transmission of the disease [6], and practices such as universal masking and social distancing have the potential to save many lives [7].
A potential concern with the use of face masks is that they may cause a reduction in speech intelligibility. We addressed this concern by investigating the effects of different types of masks on speech recognition in the context of multi-talker babble noise. We examined the effects of two homemade cloth masks, a surgical mask, and an N95 respirator, comparing performance to speech produced without a mask under conditions of both high and low levels of background noise. 152ee80cbc
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