applied sciences Article Development and Evaluation of Light-Weight Active Noise Cancellation Earphones Sen M. Kuo 1, Yi-Rou Chen 1, Cheng-Yuan Chang 1,* and Chien-Wen Lai 2 1 Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan; [email protected] (S.M.K.); [email protected] (Y.-R.C.) 2 Changhua Christian Hospital, Changhua 500, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-3-265-4838 Received: 26 May 2018; Accepted: 17 July 2018; Published: 19 July 2018 Abstract: This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance. Keywords: active noise control; light-weight earphone; natural sound; secondary path model 1. Introduction Portable devices like smart phones and MP3 players with wearable headphones or earphones are widely used by every age group from youth to the elderly. These portable devices bring convenience to people for communication and entertainment in any environment, for example in public buildings and on transportation [1]. Unfortunately, the performance of these devices will be degraded by annoying environmental noise. The acoustic noise problem has become more serious as increased numbers of equipment such as engines, blowers, fans, air-conditioners, and compressors are used in many installations, air planes, automobiles, etc. [2]. In general, there are two ways to reduce the environmental noise for portable devices: passive and active. The passive method uses thick acoustic earmuffs to cover the ear completely to block the environmental noise. However, they are relatively large, bulky, costly, and ineffective at low frequency range. The active method uses the ANC system to cancel the unwanted noise based on the principle of superposition [3]. Specifically, an anti-noise of equal amplitude and opposite phase is generated and actively combined with the primary noise, thus resulting in the cancellation of both noises. Several studies were published on the application of ANC headphones [4–11]. In addition, several ANC headphones are commercially available and have been carefully evaluated [12]. Basically, there are three types of ANC headphones on the market. The first type of headphone (type A) has earmuffs that sit on top of the ears. The second type (type B) completely encloses the ears. These two types of ANC headphones are over-ear headphones and rely on large and bulky passive earmuffs clamping on the ears to reduce high frequency noise, thus they are not portable while performing outdoor activities. Furthermore, these passive headphones are not comfortable to wear due to the pressure experienced on the side of the head restricting blood flow and hence producing discomfort over time [13]. Users experience mild headaches, feel headphones pushing the ears against the head (benign paroxysmal positional vertigo [14]), and feel clamping pressure Appl. Sci. 2018, 8, 1178; doi:10.3390/app8071178 www.mdpi.com/journal/applsci Appl.Appl. Sci. Sci. 20182018,, 88,, x 1178 FOR PEER REVIEW 22 of of 11 11 against the head (benign paroxysmal positional vertigo [14]), and feel clamping pressure on their eardrum.on their eardrum. The last type The is last an typein‐ear is ANC an in-ear earphone ANC (type earphone C) which (type was C) whichdeveloped was recently. developed Therefore, recently. lightTherefore,‐weight light-weight ANC earphones ANC earphonesare the future are thetrend; future they trend; are more they comfortable are more comfortable and they andare efficient they are forefficient users forto reduce users to environmental reduce environmental noise. noise. AsAs shown shown in in Figure Figure 11,, thisthis paperpaper developsdevelops threethree different light-weightlight‐weight ANC earphones: in in-ear,‐ear, earbud,earbud, and and clip clip phones, phones, and and compares compares their their noise noise reduction reduction with with commercially commercially available available ANC ANC headphonesheadphones (types (types A, A, B B and and C) C) of of a a leading leading brand brand company. company. The The rest rest of of the the paper paper is is organized organized as as follows.follows. Section Section II II introduces introduces the the ANC ANC algorithms algorithms used used for for the the earphones earphones and and proposes proposes using using natural natural soundsound for for modeling modeling the the required required secondary secondary path. path. Section Section III III presents presents real real-time‐time experimental experimental results results andand compares compares and and analyzes analyzes their their performance. Figure 1. Three types of earphones: clip earphone (left), earbud earphone (middle), and in‐ear Figure 1. Three types of earphones: clip earphone (left), earbud earphone (middle), and in-ear earphone (right). earphone (right). 2. ANC Algorithms for Earphones 2. ANC Algorithms for Earphones This section introduces the single‐channel adaptive feedback ANC algorithm, which requires This section introduces the single-channel adaptive feedback ANC algorithm, which requires only one error sensor for each side of the earphone. only one error sensor for each side of the earphone. 2.1.2.1. Adaptive Adaptive Feedback Feedback ANC ANC Algorithm Algorithm TheThe basic basic principle principle of of the the adaptive adaptive feedback feedback ANC ANC is is to to estimate estimate the the primary primary noise noise to to be be canceled canceled xn() Wz() toto use itit as as the the reference reference signal signalx( n) for the for adaptive the adaptive filter Wfilter(z). As shown. As inshown Figure in2, theFigure secondary 2, the secondarysignal y(n )signalgenerated yn() bygenerated the adaptive by the filter adaptive is filtered filter is by filtered the secondary by the secondary path model pathSˆ model(z) and Szˆ then() andadded then with addede(n) withmeasured en() frommeasured the error from sensor the error to synthesize sensor to the synthesize reference the signal referencex(n). The signal secondary xn(). Thesignal secondaryy(n) is generated signal yn() as is generated as L−1 y(n) = ∑L1 wl(n)x(n − l) (1) yn()l=0 wl ()( nxn l ) (1) l0 where wl(n), (l = 0, 1, ··· , L − 1) are the coefficients of W(z) at time n, and L is the filter length. whereThese filterwnl (),( coefficients l 0,1,,L L are 1) updated are the coefficients by the filtered-X of Wz() least-mean-square at time n, and L is (FXLMS) the filter algorithmlength. These [3] filterexpressed coefficients as are updated by the filtered‐X least‐mean‐square (FXLMS) algorithm [3] expressed 0 as wl(n + 1) = wl(n) + mx (n − l)e(n) (2) where m is the step size. The filteredwn signalll(1)() is wn xn ( len )() (2) where is the step size. The filtered signal Mis −1 0 x (n) ≡ sˆm(n)x(n − m) (3) ∑M 1 m=0 ˆ xn() sm ()( nxn m ) (3) m0 where sˆm(n), (m = 0, 1, ··· , M − 1) are the coefficients of the FIR filter Sˆ(z) (with length M) that is ˆ wherethe secondary snˆm (), path( m 0,1,, estimate.L M Figure 1) are2 theclearly coefficients shows that of the the FIR reference filter Sz signal() (withx(n )lengthequals M the) that primary is the ∼ secondarynoise d(n) pathif the estimate. perfect secondary Figure 2 clearly model shows is available, that the i.e., referenceSˆ(z) = S (signalz). The xn method() equals of modeling the primary the secondary path is presented in the following subsection. noise dn() if the perfect secondary model is available, i.e., Szˆ() Sz (). The method of modeling the secondary path is presented in the following subsection. Appl.Appl. Sci. Sci. 20182018, 8,, x8 ,FOR 1178 PEER REVIEW 3 of3 11 of 11 Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 11 dn() en() dn() en() x()n yn() x()n Wz() yn() Sz() Wz() Sz() Szˆ() Szˆ() Szˆ() Szˆ() x()n x()n Figure 2. Feedback ANC algorithm. FigureFigure 2. 2. FeedbackFeedback ANC ANC algorithm. algorithm. 2.2. Natural Sound for Secondary Path Modeling 2.2.2.2. Natural Natural Sound Sound for for Secondary Secondary Path Path Modeling Modeling As shown in Figure 2, the adaptive feedback ANC algorithm requires the secondary path model As shown in Figure 2, the adaptive feedback ANC algorithm requires the secondary path model Szˆ() forAs synthesizing shown in Figure the2 reference, the adaptive signal feedback x()n , and ANC compensating algorithm requires the effects the secondary of secondary path modelpath Szˆˆ() for synthesizing the reference signal x()n , and compensating the effects of secondary path Sz()S(z )forfor updating synthesizing Wz() the. The reference most popular signal x (methodn), and compensatingfor estimating the effectssecondary of secondary path is adaptive path S( z) Szfor() updating for updatingW(z) .Wz The() most. The popularmost popular method method for estimating for estimating the secondary the secondary path is path adaptive is adaptive system system identification using a white noise as the excitation signal. Unfortunately, extra random noise identification using a white noise as the excitation signal. Unfortunately, extra random noise is issystem undesired identification for consumer using electronicsa white noise such as theas earphones.excitation signal. Therefore, Unfortunately, this paper extra proposes random to noiseuse undesired for consumer electronics such as earphones. Therefore, this paper proposes to use natural naturalis undesired sound insteadfor consumer of white electronics noise for thesuch secondary as earphones.