Took Clive Cheong, Sanei Saeid, Rickard Scott, Chambers Jonathan, Dunne Stephen
School of Engineering, Electronic Engineering Research Office, Cardiff University, The Parade, Cardiff, CF24 3AA, UK.
IEEE Trans Biomed Eng. 2008 Mar;55(3):949-56. doi: 10.1109/TBME.2007.909534.
Temporomandibular joint (TMJ) sound sources are generated from the two joints connecting the lower jaw to the temporal bone. Such sounds are important diagnostic signs in patients suffering from temporomandibular disorder (TMD). In this study, we address the problem of source separation of the TMJ sounds. In particular, we examine patients with only one TMJ generating "clicks". Thereafter, we consider the TMJ sounds recorded from the two auditory canals as mixtures of clicks from the TMD joint and the noise produced by the other healthy/normal TMJ. We next exploit the statistical nonstationary nature of the TMJ signals by employing the degenerate unmixing estimation technique (DUET) algorithm, a time-frequency (T-F) approach to separate the sources. As the DUET algorithm requires the sensors to be closely spaced, which is not satisfied by our recording setup, we have to estimate the delay between the recorded TMJ sounds to perform an alignment of the mixtures. Thus, the proposed extension of DUET enables an essentially arbitrary separation of the sensors. It is also shown that DUET outperforms the convolutive Infomax algorithm in this particular TMJ source separation scenario. The spectra of both separated TMJ sources with our method are comparable to those available in existing literature. Examination of both spectra suggests that the click source has a better audible prominence than the healthy TMJ source. Furthermore, we address the problem of source localization. This can be achieved automatically by detecting the sign of our proposed mutual information estimator which exhibits a maximum at the delay between the two mixtures. As a result, the localized separated TMJ sources can be of great clinical value to dental specialists.
颞下颌关节(TMJ)声源产生于连接下颌骨与颞骨的两个关节。此类声音是颞下颌关节紊乱病(TMD)患者的重要诊断体征。在本研究中,我们探讨了颞下颌关节声音的源分离问题。具体而言,我们研究了仅一个颞下颌关节产生“咔哒声”的患者。此后,我们将从两条耳道记录到的颞下颌关节声音视为来自颞下颌关节紊乱病关节的咔哒声与另一个健康/正常颞下颌关节产生的噪声的混合。接下来,我们通过采用退化解混估计技术(DUET)算法(一种时频(T-F)方法)来利用颞下颌关节信号的统计非平稳特性以分离声源。由于DUET算法要求传感器紧密间隔,而我们的记录设置不满足这一条件,因此我们必须估计记录的颞下颌关节声音之间的延迟以对混合信号进行对齐。因此,所提出的DUET扩展实现了传感器的基本任意分离。研究还表明,在这种特定的颞下颌关节源分离场景中,DUET优于卷积信息最大化算法。用我们的方法分离出的两个颞下颌关节声源的频谱与现有文献中的频谱相当。对两个频谱的研究表明,咔哒声源比健康的颞下颌关节声源具有更好的可听显著性。此外,我们还探讨了声源定位问题。这可以通过检测我们提出的互信息估计器的符号自动实现,该估计器在两个混合信号之间的延迟处呈现最大值。结果,定位后的分离颞下颌关节声源对牙科专家具有很大的临床价值。