A Novel Unsupervised Spectral Clustering for Pure-Tone Audiograms towards Hearing Aid Filter Bank Design and Initial Configurations

The current practice of adjusting hearing aids (HA) is tiring and time-consuming for both patients and audiologists. Of hearing-impaired people, 40–50% are not satisfied with their HAs. In addition, good designs of HAs are often avoided since the process of fitting them is exhausting. To improve the fitting process, a machine learning (ML) unsupervised approach is proposed to cluster the pure-tone audiograms (PTA). This work applies the spectral clustering (SP) approach to group audiograms according to their similarity in shape. Different SP approaches are tested for best results and these approaches were evaluated by Silhouette, Calinski-Harabasz, and Davies-Bouldin criteria values. Kutools for Excel add-in is used to generate audiograms’ population, annotated using the results from SP, and different criteria values are used to evaluate population clusters. Finally, these clusters are mapped to a standard set of audiograms used in HA characterization. The results indicated that grouping the data in 8 groups or 10 results in ones with high evaluation criteria. The evaluation for population audiograms clusters shows good performance, as it resulted in a Silhouette coefficient >0.5. This work introduces a new concept to classify audiograms using an ML algorithm according to the audiograms’ similarity in shape.

Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.

Response-Only Damage Detection Approach of CFRP Gas Tanks Using Clustering and Vibrational Measurements

The advancements in the automotive, aviation, and aerospace industry have led to an increased usage of CFRP high-pressure gas tanks. In order to avoid any fatal accidents, the inspection procedures require accuracy, but also practicality, to be used in the industry. The presented work focuses on response-only metrics from vibrational experimental measurements of the CFRP tank. The power spectral density and transmittance function curves are both compared for the accuracy and ability to be used as metrics for damage detection. Along with the selection of the proper metric, an appropriate clustering algorithm that can accurately group similar states of the structure is of high importance. Two clustering algorithms, agglomerative hierarchical and spectral clustering, are employed and compared for their performance. A small Type V CFRP tank is used as an experimental structure on this benchmark problem. In order to create realistic material damage, the tank is placed on an impact system multiple times where different damage magnitudes are created. After each new state and damage magnitude on the tank, vibrational experimental data are collected. Using the collected data, all the combinations of the mentioned metrics and algorithms are executed and properly compared to evaluate their accuracy.

Protein Organization with Manifold Exploration and Spectral Clustering

We present a method to provide a biologically meaningful representation of the space of protein sequences. While billions of protein sequences are available, organizing this vast amount of information into functional categories is daunting, time-consuming and incomplete. We present our unsupervised approach that combines Transformer protein language models, UMAP graphs, and spectral clustering to create meaningful clusters in the protein spaces. To demonstrate the meaningfulness of the clusters, we show that they preserve most of the signal present in a dataset of manually curated enzyme protein families.