Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framework

In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estimators trained on clean, microphone, speech (as used in a standard large vocabulary speech recognition system) are used as a channel model at the output of which the entropy and ``dynamism'' will be measured and integrated over time through a 2-state (speech and and non-speech) hidden Markov model (HMM) with minimum duration constraints. Indeed, in the case of entropy, it is clear that, on average, the entropy at the output of the local PDF estimators will be larger for speech signals than non-speech signals presented at their input. In our case, local probabilities will be estimated from an multilayer perceptron (MLP) as used in hybrid HMM/MLP systems, thus guaranteeing the use of ``real'' probabilities in the estimation of the entropy. The 2-state speech/non-speech HMM will thus take these two dimensional features (entropy and ``dynamism'') whose distributions will be modeled through (two-dimensional) multi-Gaussian densities or an MLP, whose parameters are trained through a Viterbi algorithm.\\ Different experiments, including different speech and music styles, as well as different (a priori) distributions of the speech and music signals (real data distribution, mostly speech, or mostly music), will illustrate the robustness of the approach, always resulting in a correct segmentation performance higher than 90\%. Finally, we will show how a confidence measure can be used to further improve the segmentation results, and also discuss how this may be used to extend the technique to the case of speech/music mixtures

Robust Audio Segmentation

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsupervised techniques for three important tasks related to audio segmentation, namely speech/music segmentation, speaker change detection and speaker clustering. The speech/music segmentation technique proposed in this thesis is based on the functioning of a HMM/ANN hybrid ASR system where an MLP estimates the posterior probabilities of different phonemes. These probabilities exhibit a particular pattern when the input is a speech signal. This pattern is captured in the form of feature vectors, which are then integrated in a HMM framework. The technique thus segments the audio data in terms of {\it recognizable} and {\it non-recognizable} segments. The efficiency of the proposed technique is demonstrated by a number of experiments conducted on broadcast news data exhibiting real-life scenarios (different speech and music styles, overlapping speech and music, non-speech sounds other than music, etc.). A novel distance metric is proposed in this thesis for the purpose of finding speaker segment boundaries (speaker change detection). The proposed metric can be seen as special case of Log Likelihood Ratio (LLR) or Bayesian Information Criterion (BIC), where the number of parameters in the two models (or hypotheses) is forced to be equal. However, the advantage of the proposed metric over LLR, BIC and other metric based approaches is that it achieves comparable performance without requiring an adjustable threshold/penalty term, hence also eliminating the need for a development dataset. Speaker clustering is the task of unsupervised classification of the audio data in terms of speakers. For this purpose, a novel HMM based agglomerative clustering algorithm is proposed where, starting from a large number of clusters, {\it closest} clusters are merged in an iterative process. A novel merging criterion is proposed for this purpose, which does not require an adjustable threshold value and hence the stopping criterion is also automatically met when there are no more clusters left for merging. The efficiency of the proposed algorithm is demonstrated with various experiments on broadcast news data and it is shown that the proposed criterion outperforms the use of LLR, when LLR is used with an optimal threshold value. These tasks obviously play an important role in the pre-processing stages of ASR. For example, correctly identifying {\it non-recognizable} segments in the audio stream and excluding them from recognition saves computation time in ASR and results in more meaningful transcriptions. Moreover, researchers have clearly shown the positive impact of further clustering of identified speech segments in terms of speakers (speaker clustering) on the transcription accuracy. However, we note that this processing has various other interesting and practical applications. For example, this provides characteristic information about the data (metadata), which is useful for the indexing of audio documents. One such application is investigated in this thesis which extracts this metadata and combines it with the ASR output, resulting in Rich Transcription (RT) which is much easier to understand for an end-user. In a further application, speaker clustering was combined with precise location information available in scenarios like smart meeting rooms to segment the meeting recordings jointly in terms of speakers and their locations in a meeting room. This is useful for automatic meeting summarization as it enables answering of questions like ``who is speaking and where''. This could be used to access, for example, a specific presentation made by a particular speaker or all the speech segments belonging to a particular speaker

Robust HMM-Based Speech/Music Segmentation

In this paper we present a new approach towards high performance speech/music segmentation on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the local probability density function (PDF) estimators trained on clean microphone speech are used as a channel model at the output of which the entropy and ``dynamism'' will be measured and integrated over time through a 2-state (speech and and non-speech) hidden Markov model (HMM) with minimum duration constraints. The parameters of the HMM are trained using the EM algorithm in a completely unsupervised manner. Different experiments, including a variety of speech and music styles, as well as different segment durations of speech and music signals (real data distribution, mostly speech, or mostly music), will illustrate the robustness of the approach, which in each case achieves a frame-level accuracy greater than 94\%

An Online Audio Indexing System

This paper presents overview of an online audio indexing system, which creates a searchable index of speech content embedded in digitized audio files. This system is based on our recently proposed offline audio segmentation techniques. As the data arrives continuously, the system first finds boundaries of the acoustically homogenous segments. Next, each of these segments is classified as speech, music or {\it mixture} classes, where mixtures are defined as regions where speech and other non-speech sounds are present simultaneously and noticeably. The speech segments are then clustered together to provide consistent speaker labels. The speech and mixture segments are converted to text via an ASR system. The resulting words are time-stamped together with other metadata information (speaker identity, speech confidence score) in an XML file to rapidly identify and access target segments. In this paper, we analyze the performance at each stage of this audio indexing system and also compare it with the performance of the corresponding offline modules

Unknown-Multiple Speaker clustering using HMM

An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown \emph{a priori}. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum duration topology. The final number of clusters is determined automatically by merging closest clusters and retraining this new cluster, until a decrease in likelihood is observed. In the same framework, we also examine the effect of using only the features from highly voiced frames as a means of improving the robustness and computational complexity of the algorithm. The proposed system is assessed on the 1996 HUB-4 evaluation test set in terms of both cluster and speaker purity. It is shown that the number of clusters found often correspond to the actual number of speakers

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An HMM-Based Framework for Supporting Accurate Classification of Music Datasets

open3In this paper, we use Hidden Markov Models (HMM) and Mel-Frequency Cepstral Coecients (MFCC) to build statistical models of classical music composers directly from the music datasets. Several musical pieces are divided by instruments (String, Piano, Chorus, Orchestra), and, for each instrument, statistical models of the composers are computed.We selected 19 dierent composers spanning four centuries by using a total number of 400 musical pieces. Each musical piece is classied as belonging to a composer if the corresponding HMM gives the highest likelihood for that piece. We show that the so-developed models can be used to obtain useful information on the correlation between the composers. Moreover, by using the maximum likelihood approach, we also classied the instrumentation used by the same composer. Besides as an analysis tool, the described approach has been used as a classier. This overall originates an HMM-based framework for supporting accurate classication of music datasets. On a dataset of String Quartet movements, we obtained an average composer classication accuracy of more than 96%. As regards instrumentation classication, we obtained an average classication of slightly less than 100% for Piano, Orchestra and String Quartet. In this paper, the most signicant results coming from our experimental assessment and analysis are reported and discussed in detail.openCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, GianniCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, Giann

Modelling the effects of glucagon during glucose tolerance testing.

From Europe PMC via Jisc Publications RouterHistory: ppub 2019-12-01, epub 2019-12-12Publication status: PublishedBACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted

The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings

Meeting analysis has a long theoretical tradition in social psychology, with established practical rami?cations in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat arti?cial conditions. This paper presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesised that the conversational structure of these meetings, as indicated by sequencing and duration of vocalisations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented which determines the optimal length of vocalisation sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classi?cation as a segmentation method, and its applicability to meeting segmentation in other domains are discusse