Audio Recognition System Research Articles

A Review on Text Summarization to Develop a Live Sports Match Journalism System Using NLP

The review paper details the process of converting live speech commentary into text and subsequently generating a text summary from this transcription. Natural Language Processing (NLP) serves as the most suitable technique for this task. Sporting events attract widespread global attention, with many individuals preferring news articles over live experiences. Consequently, there's a pressing need for rapid news production following the conclusion of any sporting event. Due to constraints such as time and budget, comprehensive reporting by commentators or journalists becomes impractical. Therefore, this project holds significant promise and effectiveness. The primary aim is to automate the creation of text summaries from live sports commentary. To achieve this, a system has been devised to generate summaries from live game commentary, an area that has seen minimal focus in current research. An audio recognition system will process provided audio data, transforming it into a lengthy text format. Subsequently, a text-summarising technique will condense this transformed text into a summary.

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching.

Solfeggio is an important basic course for music majors, and audio recognition training is one of the important links. With the improvement of computer performance, audio recognition has been widely used in smart wearable devices. In recent years, the development of deep learning has accelerated the research process of audio recognition. However, there is a lot of sound interference in music teaching environment, which leads to the performance of the audio classifier that cannot meet the actual demand. In order to solve this problem, an improved audio recognition system based on YOLO-v4 is proposed, which mainly improves the network structure. First, Mel frequency cepstrum number is used to process the original audio and extract the corresponding features. Then, try to apply the YOLO-v4 model in the field of deep learning to the field of audio recognition and improve it by combining with the spatial pyramid pool module to strengthen the generalization ability of data in different audio formats. Second, the stacking method in ensemble learning is used to fuse the independent submodels of two different channels. Experimental results show that compared with other deep learning technologies, the improved YOLO-v4 model can improve the performance of audio recognition, and it has better performance in processing data of different audio formats, which shows better generalization ability.

Open Set Audio Recognition for Multi-Class Classification With Rejection

Most supervised audio recognition systems developed to this point have used a testing set which includes the same categories as the training set database. Such systems are called closed-set recognition (CSR). However, audio recognition in real applications can be more complicated, where the datasets can be dynamic, and novel categories can ceaselessly be detected. Hence, in practice, the usual methods will assign to these novel classes labels which are often incorrect. This work aims to investigate audio open-set recognition (OSR) suitable for multi-classes classification recognition, with a rejection option for classes never seen by the system. A probabilistic calibration of a support vector machine classifier is utilized and formulated under the open-set scenario. For this, it is proposed to apply a threshold technique called peak side ratio (PSR) to the audio recognition task. A candidate label is first examined by a Platt-calibrated support vector machine (SVM) to produce posterior probabilities. The PSR is then used to characterize the distribution of posterior probabilities values. This process helps to determine a threshold in order to reject or accept a particular class. Our proposed method is evaluated on different variations of open sets, using well-known metrics. Experimental results reveal that our proposed method outperforms previous OSR approaches over a wide range of openness values.

Scattering Wavelet Hash Fingerprints for Musical Audio Recognition

Fingerprint design is the cornerstone of the audio recognition systems in which aims robustness and fast retrieval. Short-term Fourier transform and Mel-spectral representations are common for the task in mind, however these extraction methods suffer from being unstable and having limited spectral-spatial resolution. Scattering wavelet transform (SWT) provides another approach to these limitations by recovering information loss, while ensuring translation invariance and stability. We propose a two-stage feature extraction framework using SWT coupled with deep Siamese hashing model for musical audio recognition. Similarity-preserving hashes are the final fingerprints and in the projected embedding space, similarity is defined by a distance metric. Hashing model is trained by roughly aligned and non-matching audio snippets to model musical audio data via two-layer scattering spectrum. Our proposed framework provides competitive performance results to identify audio signals superimposed with environmental noise which can be modeled as real-world obstacles for music recognition. With a very compact storage footprint (256 bytes/sec.), we achieve 98.2% ROC AUC score on GTZAN dataset

Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK

In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.

A multi features fusion support vector machine for classification of emotion issue in the design of an audio recognition system

Most state-of-the-art automatic speech emotion recognition rely on utterance level statistics of features. In this study, spoken utterances are represented by a set of statistics from different features computed over all frames. Therefore, for exploiting the complementary emotion-specific information provided by individual features (spectral, prosodic and voice quality features), intelligent combination of features is expected. In this work, we use contour-based low-level descriptors to extract features from the emotional data and then fuse the evidences provided by these features. Finally, multi-class SVM modelling is performed directly at the output of the extracted features. The experiments were carried out on the Berlin corpus consisting of six basic emotions: sadness, boredom, neutral, fear, happiness, anger and the neutral state (no emotion). The results demonstrate that on the average, the features obtained from different information streams and combined at the decision level outperforms the single features or the features combined at the feature level in terms of classification accuracy.

Algorithm of Abnormal Audio Recognition Based on Improved MFCC

Characteristics extraction has a great effect on the audio training and recognition in the audio recognition system. MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate. For the situation that MFCC has a large amount of computation, an improved algorithm MFCC_E is introduced. The computation of MFCC_E is reduced by 50% compared with the standard algorithm MFCC, and it make the hardware implementation is easy. The experimental result indicated that MFCC_E and MFCC have the same recognition rate roughly, yet the computational complexity of MFCC_E is much smaller.

Abnormal audio recognition algorithm based on MFCC and short-term energy

Concerning the high complexity and low rate in abnormal audio recognition,the abnormal audio recognition system based on the Mel-Frequency Cepstrum Coefficients(MFCC)and short-term energy was proposed.This feature vector made the Gaussian Mixture Model(GMM)classifier outperform MFCC and Differential MFCC features in classification.The classifier can achieve an average recognition rate of more than 90%,and small computational complexity.The steps of system implementation were elaborated.The simulation results prove the effectiveness of the proposed algorithm.