Audio Processing Applications Research Articles

Efficient Implementation of Reconfigurable Warped Digital Filters With Variable Low-Pass, High-Pass, Bandpass, and Bandstop Responses

In this brief, an efficient implementation of reconfigurable warped digital filter with variable low-pass, high-pass, bandpass, and bandstop responses is presented. The warped filters, obtained by replacing each unit delay of a digital filter with an all-pass filter, are widely used for various audio processing applications. However, warped filters require first-order all-pass transformation to obtain variable low-pass or high-pass responses, and second-order all-pass transformation to obtain variable bandpass or bandstop responses. To overcome this drawback, the proposed method combines the warped filters with the coefficient decimation technique. The proposed architecture provides variable low-pass or high-pass responses with fine control over cut-off frequency and variable bandwidth bandpass or bandstop responses at an arbitrary center frequency without updating the filter coefficients or filter structure. The design example shows that the proposed variable digital filter is simple to design and offers substantial savings in gate counts and power consumption over other approaches.

Conditional Random Fields in Speech, Audio, and Language Processing

Conditional random fields (CRFs) are probabilistic sequence models that have been applied in the last decade to a number of applications in audio, speech, and language processing. In this paper, we provide a tutorial overview of CRF technologies, pointing to other resources for more in-depth discussion; in particular, we describe the common linear-chain model as well as a number of common extensions within the CRF family of models. An overview of the mathematical techniques used in training and evaluating these models is also provided, as well as a discussion of the relationships with other probabilistic models. Finally, we survey recent work in speech, audio, and language processing to show how the same CRF technology can be deployed in different scenarios.

Multichannel massive audio processing for a generalized crosstalk cancellation and equalization application using GPUs

Multichannel acoustic signal processing has undergone major development in recent years due to the increased complexity of current audio processing applications, which involves the processing of multiple sources, channels, or filters. A general scena

Signal-Driven Window-Length Adaptation for Sinusoid Detection in Polyphonic Music

Audio processing applications that use short-time signal analysis techniques typically utilize fixed window duration single- or multi-resolution analyses. However, different real-world signal conditions such as polyphony and non-stationarity, manifested as musical accompaniment and pitch-modulations, respectively, in the context of music content analysis, require varying data window lengths for reliable processing. In this paper, we investigate the use of signal sparsity for adapting analysis window lengths. Adaptive-window analysis driven by different measures of sparsity applied to the local spectrum, such as kurtosis and Gini index, is evaluated and shown to be superior to fixed-window analysis in terms of sinusoid detection and frequency estimation for simulated and real signals. A window main-lobe matching method for sinusoid detection is also shown to be more robust to signal conditions such as polyphony and frequency modulation relative to other methods.

A Low-Complexity Spectro-Temporal Distortion Measure for Audio Processing Applications

Perceptual models exploiting auditory masking are frequently used in audio and speech processing applications like coding and watermarking. In most cases, these models only take into account spectral masking in short-time frames. As a consequence, undesired audible artifacts in the temporal domain may be introduced (e.g., pre-echoes). In this article we present a new low-complexity spectro-temporal distortion measure. The model facilitates the computation of analytic expressions for masking thresholds, while advanced spectro-temporal models typically need computationally demanding adaptive procedures to find an estimate of these masking thresholds. We show that the proposed method gives similar masking predictions as an advanced spectro-temporal model with only a fraction of its computational power. The proposed method is also compared with a spectral-only model by means of a listening test. From this test it can be concluded that for non-stationary frames the spectral model underestimates the audibility of introduced errors and therefore overestimates the masking curve. As a consequence, the system of interest incorrectly assumes that errors are masked in a particular frame, which leads to audible artifacts. This is not the case with the proposed method which correctly detects the errors made in the temporal structure of the signal.

Single-Channel Source Separation Using Complex Matrix Factorization

Nonnegative matrix factorization is gaining popularity in speech and audio processing applications. Performing nonnegative matrix factorization on a complex-valued short-time Fourier transform, however, makes assumptions on the signal, such as additivity in the magnitude domain, potentially degrading the results. One application where these assumptions can cause a problem is in single-channel source separation of overlapping speech. In this paper, we present how this problem can be solved by incorporating phase estimation via complex matrix factorization. Another challenge in source separation is how to select reconstruction bases for optimal separation. In this paper, we compare the most common method with a new, simpler method of finding bases that does not share many of the challenges of the current, established method. The paper will conclude by comparing nonnegative with complex matrix factorization as well as the previous and new methods for finding bases on the task of automatic speech recognition of single-channel two-talker overlapping speech.

Compact and low-power continuous-time derivative circuit

A new circuit to compute a continuous-time derivative is introduced. Comprising only one capacitor and six to eight transistors, this derivative circuit occupies very little real estate and consumes limited power, making it particularly well suited to low-power and array applications such as imager systems and sub-banded audio-processing applications. A description of the operation of this circuit is provided along with results from a circuit fabricated in a standard 0.5 µm CMOS process.

Modeling nuisance variabilities with factor analysis for GMM-based audio pattern classification

Abstract Audio pattern classification represents a particular statistical classification task and includes, for example, speaker recognition, language recognition, emotion recognition, speech recognition and, recently, video genre classification. The feature being used in all these tasks is generally based on a short-term cepstral representation. The cepstral vectors contain at the same time useful information and nuisance variability, which are difficult to separate in this domain. Recently, in the context of GMM-based recognizers, a novel approach using a Factor Analysis (FA) paradigm has been proposed for decomposing the target model into a useful information component and a session variability component. This approach is called Joint Factor Analysis (JFA), since it models jointly the nuisance variability and the useful information, using the FA statistical method. The JFA approach has even been combined with Support Vector Machines, known for their discriminative power. In this article, we successfully apply this paradigm to three automatic audio processing applications: speaker verification, language recognition and video genre classification. This is done by applying the same process and using the same free software toolkit. We will show that this approach allows for a relative error reduction of over 50% in all the aforementioned audio processing tasks.

Intelligent Audio, Speech, and Music Processing Applications

Intelligent Audio, Speech, and Music Processing Applications