Multi-pitch estimation is critical in many applications, including computational auditory scene analysis (CASA), speech enhancement/separation and mixed speech analysis; however, despite much effort, it remains a challenging problem. This paper uses the PEFAC algorithm to extract features and proposes the use of recurrent neural networks with bidirectional Long ShortTerm Memory (RNN-BLSTM) to m...

Recent psychophysical studies suggest that human listeners do not segregate concurrent sounds by grouping frequency regions that have a common interaural time difference (ITD). However, such an approach is adopted by most computational auditory scene analysis (CASA) systems that use binaural cues. Here, we propose a CASA system that separates a target speech signal from a noise interferer, but ...

In this paper, we propose a computational auditory scene analysis (CASA)–based front–end for two–microphone speech recognition in a car environment. One of the important issues associated with CASA is the accurate estimation of mask information for target speech separation within multiple microphone noisy speech. For such a task, the time–frequency mask information is compensated through the si...

In late 2006, the NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA, McLaughlin et al. 2005) began its first integrated project (IP1, Brotzge et al. 2007). CASA-IP1 consists of a network of four x-band dual-polarization Doppler radars in southwest Oklahoma. During the spring of 2007, this radar network collected data from a number of severe con...

Knowledge of the probable origin and behaviour of arsenic certainly gives valuable insights into the potential for transfer in the environment and of the risks involved in mining sites. Sequential extraction analyses are common experiments often used to study the origin and behaviour of potentially toxic elements. The method, however, presents some deficiencies, including labor-intensive proced...

We propose a computational auditory scene analysis (CASA) model for monaural speech segregation. It deals with low-frequency and high-frequency signals differently. For high-frequency signals, it generates segments based on common amplitude modulation (AM) and groups them according to AM repetition rates. This model performs substantially better than previous CASA systems.

abstract zinc (zn) is an essential plant nutrient. knowledge about distribution of zn between its different chemical forms is useful in understanding the chemistry of this element in soil and also in development of soil testing procedures. the present study was conducted to obtain such information about distribution of zinc forms in 20 soil samples of lorestan province by the use of a sequentia...

Recent psychophysical studies suggest that human listeners do not segregate concurrent sounds by grouping frequency regions that have a common interaural time difference (ITD). However, such an approach is adopted by most computational auditory scene analysis (CASA) systems that use binaural cues. Here, we propose a CASA system that separates a target speech signal from a noise interferer, but ...

What is the computational goal of auditory scene analysis? This is a key issue to address in the Marrian information-processing framework. It is also an important question for researchers in computational auditory scene analysis (CASA) because it bears directly on how a CASA system should be evaluated. In this chapter I discuss different objectives used in CASA. I suggest as a main CASA goal th...