{'fraction_non_alphanumeric': 0.024844720496894408, 'fraction_numerical': 0.007246376811594203, 'mean_word_length': 5.442222222222222, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evidence-based design of a noise-management algorithm

design (EBD) is an essential component of the productdevelopment process. It is aimed at: (1) ensuring that the product is reliable and functions as intended, and (2) ensuring that the product provides a measurable, real-world benefit to users. From this point of view, a successful noise-management algorithm must improve subjective comfort in noise without degrading objective speech intelligibility for the hearing aid user.1,2 Although these objectives seem straightforward, earlier iterations of noise-management algorithms had difficulty meeting both goals. First-generation algorithms attempted to apply an overall gain reduction in response to broadband, unmodulated “noise” at relatively high input levels.3 While these early algorithms were able to improve subjective comfort in response to higher levels of environmental noise, they had two main drawbacks: slower time constants and a lack of specificity. The result was an inability to decrease gain for noise without also decreasing gain for speech.4 Second-generation algorithms attempted to correct this problem by using faster time constants and varying the input levels at which gain reduction for noise was applied.5 These algorithms had limited success, as researchers had difficulty demonstrating improved speech understanding in noise or user preference for these types of noise-reduction schemes in the field.6-8 As third-generation noise-management algorithms begin to emerge, manufacturers are trying various methods to improve their performance. Yet, proving efficacy for this type of technology remains elusive.9,10 Although some studies have shown improved comfort and sound quality1,11 and others have objectively demonstrated enhanced or unchanged speech understanding in noise, the debate over their benefits to users is far from over.11,12 However, as the potential for improved patient benefit from a third-generation algorithm grows, Starkey Laboratories has developed a fast-acting noise adaptation system called Voice iQ. Voice iQ was initially developed at the Starkey Hearing Research Center (SHRC) in Berkeley, CA, with the intent to create a noise-reduction algorithm fast enough to reduce gain during the pauses in speech over a broad range of input levels, while achieving the combined goals of maintaining comfort without reducing speech intelligibility in noise. The system detects and continually monitors the spectral and temporal characteristics of both speech and noise levels to estimate signal-to-noise ratio (SNR). Fast-acting gain adaptation is applied to unmodulated noise sources during the gaps in speech, even at lower input levels. Voice iQ initiates gain adaptation at a +5-dB SNR and reaches its maximum potential for gain reduction at 0-dB SNR, attempting to optimize performance as the listening environment becomes more challenging.