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the use of the I<inf>0</inf>-sinh window for spectrum analysis","{\"openalex\":\"W2103850068\",\"doi\":\"10.1109/tassp.1980.1163349\"}","2026-05-22T20:22:34.447123797","2026-05-22T20:22:34.447125648","doi","10.1109/tassp.1980.1163349","arxivId","openalexId","W2103850068","abstractText","Closed form expressions for main-lobe width, modified main-lobe width, and relative sidelobe amplitude are given for the I_{0}- \\sinh window function. These formulas facilitate exploring the tradeoff between record length, spectral resolution, and leakage in digital spectrum analysis. An especially simple empirical approximation relating main-lobe width and relative sidelobe amplitude is given.","publishedDate","1980-02-01","publishedYear",1980,"venue","IEEE Transactions on Acoustics Speech and Signal Processing",236,"authors","keywords",[190,191,192],"pdfUrl","topVenue","Advanced Electrical Measurement Techniques","Blind Source Separation Techniques","Image and Signal Denoising Methods","paper_W2123133018","Some useful properties of Teager's energy operators","{\"openalex\":\"W2123133018\",\"doi\":\"10.1109/icassp.1993.319457\"}","2026-05-20T07:41:03.197098061","2026-05-20T07:41:03.20040554","10.1109/icassp.1993.319457","W2123133018","Teager's energy operators are defined in both the continuous and discrete domains and are very useful tools for analyzing single component signals from an energy point of view. A number of important properties of these operators are shown that make it possible to determine the energy functions of quite complicated functions, provided these functions can be expressed as products of simpler functions, this operation of function multiplication being typical of a modulation process. Some of the eigenfunction properties of the energy operator that illustrate the special role of the trigonometric, Gaussian, and single soliton functions are also given.< >","1993-01-01",1993,"IEEE International Conference on Acoustics Speech and Signal Processing",344,[206,192,207],"Mathematical Analysis and Transform Methods","Machine Fault Diagnosis Techniques","paper_W2123861288","Nonlinear feature based classification of speech under stress","{\"openalex\":\"W2123861288\",\"doi\":\"10.1109/89.905995\"}","2026-05-19T14:05:24.235079913","2026-05-19T14:05:24.234984018","10.1109/89.905995","W2123861288","Abstract—Studies have shown that variability introduced by stress or emotion can severely reduce speech recognition accuracy. Techniques for detecting or assessing the presence of stress could help improve the robustness of speech recognition systems. Although some acoustic variables derived from linear speech production theory have been investigated as indicators of stress, they are not always consistent. In this paper, three new features derived from the nonlinear Teager energy operator (TEO) are investigated for stress classification. It is believed that the TEO based features are better able to reflect the nonlinear airflow structure of speech production under adverse stressful conditions. The features proposed include TEO-decomposed FM variation (TEO-FM-Var), normalized TEO autocorrelation envelope area (TEO-Auto-Env), and critical band based TEO autocorrelation envelope area (TEO-CB-Auto-Env). The proposed features are evaluated for the task of stress classification using simulated and actual stressed speech and it is shown that the TEO-CB-Auto-Env feature outperforms traditional pitch and mel-frequency cepstrum coefficients (MFCC) substantially. Performance for TEO based features are maintained in both text-dependent and text-independent models, while performance of traditional features degrades in text-independent models. Overall neutral versus stress classification rates are also shown to be more consistent across different stress styles. Index Terms—Human factors, nonlinear speech feature, speech analysis, speech recognition, stress classification, Teager energy operator (TEO). I.","2001-03-01",2001,"IEEE Transactions on Speech and Audio Processing",406,[221,222,223],"Speech Recognition and Synthesis","Speech and Audio Processing","Phonetics and Phonology Research","paper_W2114537326","Energy separation in signal modulations with application to speech analysis","{\"openalex\":\"W2114537326\",\"doi\":\"10.1109/78.277799\"}","2026-05-15T01:09:52.322643961","2026-05-21T00:42:56.183043712","10.1109/78.277799","W2114537326","An efficient solution to the fundamental problem of estimating the time-varying amplitude envelope and instantaneous frequency of a real-valued signal that has both an AM and FM structure is provided. Nonlinear combinations of instantaneous signal outputs from the energy operator are used to separate its output energy product into its AM and FM components. The theoretical analysis is done first for continuous-time signals. Then several efficient algorithms are developed and compared for estimating the amplitude envelope and instantaneous frequency of discrete-time AM-FM signals. These energy separation algorithms are used to search for modulations in speech resonances, which are modeled using AM-FM signals to account for time-varying amplitude envelopes and instantaneous frequencies. The experimental results provide evidence that bandpass-filtered speech signals around speech formants contain amplitude and frequency modulations within a pitch period.< >","IEEE Transactions on Signal Processing",765,[222,191,192],"paper_W1538482441","On a simple algorithm to calculate the 'energy' of a signal","{\"openalex\":\"W1538482441\",\"doi\":\"10.1109/icassp.1990.115702\"}","2026-05-14T10:53:31.257455854","2026-05-21T14:27:22.201437262","10.1109/icassp.1990.115702","W1538482441","A simple algorithm is derived that permits on-the-fly calculation of the energy required to generate, in a certain sense, a signal. The results of applying this algorithm to a number of well-known signals are shown. Some of the invariance and noise properties of the algorithm are derived and verified by simulation. The implementation of the algorithm and its application to speech processing are briefly discussed.< >","2002-12-04",2002,"International Conference on Acoustics, Speech, and Signal Processing",1141,[248,249,222],"Neural Networks and Applications","Scientific Research and Discoveries",{"_59":251,"_61":252},[],[253],{"_64":254,"_66":255,"_68":256,"_70":257},1783608769714,{"_73":259,"_75":76,"_77":254,"_78":-5,"_79":26,"_80":26,"_81":26,"_82":-5,"_83":-5,"_84":85,"_35":86,"_87":88},[72,258],"[\"portal\",\"industries\"]","industries",[260,261,262,263,264,265,266,267,268,269,270,271],{"_15":333,"_29":334,"_31":-5,"_32":335,"_33":336,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":337,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":334,"_58":336},{"_15":329,"_29":330,"_31":-5,"_32":-5,"_33":331,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":332,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":330,"_58":331},{"_15":323,"_29":324,"_31":-5,"_32":325,"_33":326,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":327,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":324,"_58":326},{"_15":319,"_29":320,"_31":-5,"_32":-5,"_33":321,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":322,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":320,"_58":321},{"_15":315,"_29":316,"_31":-5,"_32":-5,"_33":317,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":318,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":316,"_58":317},{"_15":309,"_29":310,"_31":-5,"_32":311,"_33":312,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":313,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":310,"_58":312},{"_15":304,"_29":305,"_31":-5,"_32":-5,"_33":306,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":307,"_284":26,"_285":308,"_286":26,"_287":-5,"_57":305,"_58":306},{"_15":300,"_29":301,"_31":-5,"_32":-5,"_33":302,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":303,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":301,"_58":302},{"_15":296,"_29":297,"_31":-5,"_32":-5,"_33":298,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":299,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":297,"_58":298},{"_15":292,"_29":293,"_31":-5,"_32":-5,"_33":294,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":295,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":293,"_58":294},{"_15":288,"_29":289,"_31":-5,"_32":-5,"_33":290,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":291,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":289,"_58":290},{"_15":272,"_29":273,"_31":-5,"_32":-5,"_33":274,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":275,"_42":-5,"_44":-5,"_276":277,"_278":-5,"_279":76,"_280":281,"_282":283,"_284":26,"_285":85,"_286":26,"_287":-5,"_57":273,"_58":274},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[314],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[328],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[338,339],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_342":24},"SITE_BASE_URL"]

J. Kaiser 人物

J. Kaiser · 相关文章