# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 1 Introduction  
The people with impaired hearing have difficulty hearing because they have a higher hearing threshold and narrower dynamic range than a person with normal hearing [1, 2]. Consequently, hearing aids are worn as a way to reduce the difficulties caused by hearing loss, with hearing aids compensating for that loss [3]. Hearing aids are small electronic devices that amplify speech in noisy environments and improve the quality of communication for the hearing impaired. Daily life is full of sound, and hearing aid technology is being constantly developed to reduce noise such as car horns, restaurants noise, the buzzing from electrical equipment, and random voices in the surroundings. Consequently, hearing aid technologies need to reduce environmental noise and amplify voices in order to improve speech intelligibility. However, one of the major complaints of hearing aid users is the inability of hearing aids to completely reduce environmental noise, along with the associated amplification of unexpected noise together with speech [4]. This is because the actual hearing aid operates in scenarios of various, irregular, and random environmental noise. Digital hearing aids operated various audio signal processing algorithms, among which the speech enhancement algorithms are extremely important. Furthermore, speech enhancement algorithms for hearing aids, which are dissimilar to general signal processing algorithms, need to be realistic and specific due to the expectations of people with impaired hearing. A typical speech enhancement algorithm for hearing aids classifies environmental noise near the hearing aid itself, estimating the noise power from the input signal, in order to reduce the estimated noise. For example, popular hearing aid noise classification algorithms include the support vector machine (SVM) [5] and gaussian mixture model (GMM) [6], popular noise estimation algorithms include the minimum statistics (MS) [7], minima controlled recursive averaging (MCRA) [8], and improved minima controlled recursive averaging (IMCRA) [9] algorithms, and popular noise reduction algorithms include the spectral subtraction (SS) [10], noise reduction using the wiener filter [11], the minimum mean square error short-time spectral amplitude (MMSE-STSA) [12], and the log minimum mean square error (logMMSE) algorithms [13]. [PERSON] et al. [14] used the minimum mean square error-log scale amplitude (MMSE-LSA) estimator and modified for improving hearing aids speech perception. [PERSON] et al. [15] proposed a noise reduction and speech enhancement algorithm for hearing aid using the matrix wiener filter and compared the performance with multichannel wiener filter. [PERSON] et al. [16] used a power spectrum estimation and calculated the gain using spectral subtraction for speech enhancement of hearing aids, and [PERSON] and [PERSON] [17] proposed a speech enhancement algorithm for hearing aids using the IMCRA for noise estimation and removing the noise using logMMSE. After that, the noises were estimated using adaptive IMCRA parameters based on the noise classification using GMM, the parameters of which were used for noise reduction [18]. Though various conventional algorithms for hearing aids were proposed to improve the speech quality, they did not apply deep learning techniques for speech enhancement of hearing aids, which was known to be effective for noise classification and speech recognition technology. The speech quality evaluation components of most typical speech enhancement algorithms for hearing aids use popular noise databases which are unnatural and homogenized, such as the NOISEX-92 database [19], the NOIZEUS database [20], the CHiME background noise data [21], and the AURORA-2 corpus [22]. However, the actual hearing aids operates in various irregular, and random noisy environments. The actual environmental noise may comprise two or more types of noise--for example, the random babble of subway sounds--and unexpected sounds may suddenly occur.