{"title":"The Spectral Nature of Maximum Likelihood Noise Compensated Linear Prediction","authors":"L. Weruaga, L. Dimitrov","doi":"10.1109/TASL.2013.2255277","DOIUrl":null,"url":null,"abstract":"The effects of noise in autoregressive (AR) analysis (or linear prediction) and its compensation (NCAR) has been commonly carried out in the time domain under the least square (LS) criterion. This paper studies the adequacy of such an approach by means of a comparative analysis with selected frequency-based NCAR methods. In particular, the maximization of the spectral likelihood (ML) results in a proper optimization problem that is easy to solve and brings useful insights into the rationale of the NCAR problem. On the contrary, popular time-based NCAR methods are shown in the paper to be designed, in the ML context, around ill-conditioned criteria, requiring constraints to guarantee stable solutions. The statistical analysis on a realistic scenario as well as an experiment on speech enhancement complement this analysis.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2255277","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2255277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The effects of noise in autoregressive (AR) analysis (or linear prediction) and its compensation (NCAR) has been commonly carried out in the time domain under the least square (LS) criterion. This paper studies the adequacy of such an approach by means of a comparative analysis with selected frequency-based NCAR methods. In particular, the maximization of the spectral likelihood (ML) results in a proper optimization problem that is easy to solve and brings useful insights into the rationale of the NCAR problem. On the contrary, popular time-based NCAR methods are shown in the paper to be designed, in the ML context, around ill-conditioned criteria, requiring constraints to guarantee stable solutions. The statistical analysis on a realistic scenario as well as an experiment on speech enhancement complement this analysis.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.