Acoustic scenes (or soundscapes) can be composed of various background and foreground sound events. Classification of soundscapes have to deal with the overlap of these sound events. In this work, we propose to reduce this overlap by considering foreground sound events and background sound events as multiple views of the soundscape. Robust principal components analysis is used to decompose a soundscape into the background view and the foreground view. We can control the amount of information retained in this decomposition by using the subspace projection technique of nuisance attribute projection. We represent the audio samples as features from a convolutional neural network, and view-invariant representations are derived using a deep neural network based multi-view learning algorithm. Experimental results demonstrate the effectiveness of our proposed method on standard datasets for acoustic scene classification.

State-of-the-art spoken language identification (LID) networks are vulnerable to channel-mismatch that occurs due to the differences in the channels used to obtain the training and testing samples. The effect of channel-mismatch is severe when the training dataset contains very limited channel diversity. One way to address channel-mismatch is by learning a channel-invariant representation of the speech using adversarial multi-task learning (AMTL). But, AMTL approach cannot be used when the training samples do not contain the corresponding channel labels. To address this, we propose an auxiliary within-sample similarity loss (WSSL) which encourages the network to suppress the channel-specific contents in the speech. This does not require any channel labels. Specifically, WSSL gives the similarity between a pair of embeddings of same sample obtained by two separate embedding extractors. These embedding extractors are designed to capture similar information about the channel, but dissimilar LID-specific information in the speech. Furthermore, the proposed WSSL improves the noise-robustness of the LID-network by suppressing the background noise in the speech to some extent. We demonstrate the effectiveness of the proposed approach in both seen and unseen channel conditions using a set of datasets having significant channel-mismatch.

State-of-the-art systems for spoken language identification (LID) use i-vector or embedding extracted using a deep neural network (DNN) to represent the utterance. These fixed-length representations are obtained without explicitly considering the relevance of individual frame-level feature vectors in deciding the class label. In this paper, we propose a new method to represent the utterance that considers the relevance of the individual frame-level features. The proposed representation can also preserve the locally available LID-specific information in the input features to some extent. To better utilize the local-level information in the new representation, we propose a novel segment-level matching kernel based support vector machine (SVM) classifier. The proposed representation of the utterance based on the relevance of frame-level features improves the robustness of the LID system to different background noise conditions in the speech. The experiments conducted on speech with different background conditions show that the proposed approach performs better than state-of-the-art approaches in noisy speech and performs similarly to the state-of-the-art systems in clean speech condition.

In this paper, we propose a statistical framework to prune feature maps in 1-D deep convolutional networks. SoundNet is a pre-trained deep convolutional network that accepts raw audio samples as input. The feature maps generated at various layers of SoundNet have redundancy, which can be identified by statistical analysis. These redundant feature maps can be pruned from the network with a very minor reduction in the capability of the network. The advantage of pruning feature maps, is that computational complexity can be reduced in the context of using an ensemble of classifiers on the layers of SoundNet. Our experiments on acoustic scene classification demonstrate that ignoring 89% of feature maps reduces the performance by less than 3% with 18% reduction in computational complexity.

We propose a supervised, convex representation based audio hashing framework for bird species classification. The proposed framework utilizes archetypal analysis, a matrix factorization technique, to obtain convex-sparse representations of a bird vocalization. These convex representations are hashed using Bloom filters with non-cryptographic hash functions to obtain compact binary codes, designated as conv-codes. The conv-codes extracted from the training examples are clustered using class-specific k-medoids clustering with Jaccard coefficient as the similarity metric. A hash table is populated using the cluster centers as keys while hash values/slots are pointers to the species identification information. During testing, the hash table is searched to find the species information corresponding to a cluster center that exhibits maximum similarity with the test conv-code. Hence, the proposed framework classifies a bird vocalization in the conv-code space and requires no explicit classifier or reconstruction error calculations. Apart from that, based on min-hash and direct addressing, we also propose a variant of the proposed framework that provides faster and effective classification. The performances of both these frameworks are compared with existing bird species classification frameworks on the audio recordings of 50 different bird species.

This paper focuses on the problem of bird species identification using audio recordings. Following recent developments in deep learning, we propose a multi-layer alternating sparse-dense framework for bird species identification. Temporal and frequency modulations in bird vocalizations are captured by concatenating frames of spectrograms, resulting in a high dimensional super-frame based representation. These super-frame representations are highly sparse. Hence, we propose to use random projections to compress these super-frames. This is followed by class-specific archetypal analysis, employed on these compressed super-frames for acoustic modeling, to obtain a convex-sparse representation. These convex-sparse representations are referred as compressed convex spectral embeddings (CCSE). It is observed that these representations efficiently capture species-specific discriminative information. Experimental results show compelling evidence that the proposed approach shows performance comparable to existing methods such as deep neural networks (DNN) and dynamic kernel based SVMs.

In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.

A voice activity detector (VAD) plays a vital role in robust speaker verification, where energy VAD is most commonly used. Energy VAD works well in noise-free conditions but deteriorates in noisy conditions. One way to tackle this is to introduce speech enhancement preprocessing. We study an alternative, likelihood ratio based VAD that trains speech and nonspeech models on an utterance-by-utterance basis from mel-frequency cepstral coefficients (MFCCs). The training labels are obtained from enhanced energy VAD. As the speech and nonspeech models are re-trained for each utterance, minimum assumptions of the background noise are made. According to both VAD error analysis and speaker verification results utilizing state-of-the-art i-vector system, the proposed method outperforms energy VAD variants by a wide margin. We provide open-source implementation of the method.

We study the problem of vocal effort mismatch in speaker verification. Changes in speaker’s vocal effort induce changes in fundamental frequency (F0) and formant structure which introduce unwanted intra-speaker variations to features. We compare seven alternative spectrum estimators in the context of melfrequency cepstral coefficient (MFCC) extraction for speaker verification. The compared variants include traditional FFT spectrum and six parametric all-pole models. Experimental results on the NIST 2010 speaker recognition evaluation (SRE) corpus utilizing both GMM-UBM and more recent GMM supervector classifier indicate that spectrum estimation has a considerable impact on speaker verification accuracy under mismatched vocal effort conditions. The highest recognition accuracy was achieved using a particular variant of temporally weighted all-pole model, stabilized weighted linear prediction (SWLP). Index Terms: speaker recognition, vocal effort mismatch, spectrum estimation

Total variability modeling, based on i-vector extraction of converting a variable-length sequence of feature vectors into a fixed-length i-vector, is currently an adopted parametrization technique for state of-the-art speaker verification systems. However, when the number of the feature vectors is low, uncertainty in the i-vector representation as a point estimate of the linear-Gaussian model is understandably problematic. It is known that the zeroth and first order sufficient statistics, given the hyperparameters, completely characterize the extracted i-vectors. In this study we propose to use a minimax strategy to estimate the sufficient statistics in order to increase the robustness of the extracted i-vectors. We show by experiments that the proposed minimax technique can improve over the baseline system from 9.89 % to 7.99 % on the NIST SRE 2010 8conv-10sec task.

Human judgment is the final authority in forensic speaker recognition, but the use of modern speaker verification systems with accurate algorithms to perform the task under various circumstances has a huge potential to help the expert. The ultimate goal is to improve the accuracy of automatic systems when challenging data is provided and find a methodology for human-aided speaker recognition systems. This work presents an evaluation of speaker recognition carried out by human listeners and a gender dependent i-vector recognizer with a strategy for fusion of the decision process. Our experiments with HASR 2010 and HASR 2012 data indicate complementarity in

The i-vector representation and PLDA classifier have shown state-of-the-art performance for speaker recognition systems. The availability of more than one enrollment utterance for a speaker allows a variety of configurations which can be used to enhance robustness to noise. The well-known technique of multicondition training can be utilized at different stages of the system, including enrollment and classifier training. We also study the effect of mismatched training, averaging and length normalization. Our study indicates that multicondition training of the PLDA model, and if possible the enrollment i-vectors are the most important to achieve good performance in noisy evaluation data. Index Terms: Speaker verification, i-vector, PLDA, multicondition training

Popular features for speech processing, such as mel-frequency cepstral coefficients (MFCCs), are derived from the short-term magnitude spectrum, whereas the phase spectrum remains un-used. While the common argument to use only the magnitude spectrum is that the human ear is phase-deaf, phase-based fea-tures have remained less explored due to additional signal pro-cessing difficulties they introduce. A useful representation of the phase is the group delay function, but its robust computa-tion remains difficult. This paper advocates the use of group delay functions derived from parametric all-pole models instead of their direct computation from the discrete Fourier transform. Using a subset of the vocal effort data in the NIST 2010 speaker recognition evaluation (SRE) corpus, we show that group delay features derived via parametric all-pole models improve recog-nition accuracy, especially under high vocal effort. Addition-ally, the group delay features provide comparable or improved accuracy over conventional magnitude-based MFCC features. Thus, the use of group delay functions derived from all-pole models provide an effective way to utilize information from the phase spectrum of speech signals. Index Terms: speaker verification, group delay functions, high vocal effort

I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE’12. Noisy test segments, uneven multi-session training, variable enrollment duration, and the issue of open-set identification were actively discussed leading to various solutions integrated to the I4U submission. The joint submission and several of its 17 sub-systems were among top-performing systems. We summarize the lessons learnt from this large-scale effort.

In recent years, there have been significant advances in the field of speaker recognition that has resulted in very robust recognition systems. The primary focus of many recent developments have shifted to the problem of recognizing speakers in adverse conditions, e.g in the presence of noise/reverberation. In this paper, we present the UMD-JHU speaker recognition system applied on the NIST 2010 SRE task. The novel aspects of our systems are: 1) Improved performance on trials involving different vocal effort via the use of linear-scale features; 2) Expected improved recognition performance in the presence of reverberation and noise via the use of frequency domain perceptual linear predictor and cortical features; 3) A new discriminative kernel partial least squares (KPLS) framework that complements state-of-the-art back-end systems JFA and PLDA to aid in better overall recognition; and 4) Acceleration of JFA, PLDA and KPLS back-ends via distributed computing. The individual components of the system and the fused system are compared against a baseline JFA system and results reported by SRI and MIT-LL on SRE2010.