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ویرایش: نویسندگان: Cox. Trevor J., Li. Francis F سری: ISBN (شابک) : 9781466593886, 9781466593893 ناشر: CRC Press سال نشر: 2019 تعداد صفحات: 228 [243] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 4 Mb
در صورت تبدیل فایل کتاب Digital signal processing in audio and acoustical engineering به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب پردازش سیگنال دیجیتال در مهندسی صدا و آکوستیک نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Cover Half Title Title Page Copyright Page Contents Preface About the Authors Chapter 1: Acoustic Signals and Audio Systems 1.1. Signals and Systems 1.2. Types of Systems by Properties 1.3. Types of Signals 1.3.1. Deterministic Signals 1.3.2. Some Special Testing Signals 1.3.3. Random Signals 1.4. Statistics of Random Signals 1.4.1. Probability Density Function and Moments 1.4.2. Lag Statistical Analysis and Correlation Functions 1.4.3. Gaussian Distribution and Central Limit Theorem 1.5. Signals in Transformed Frequency Domains 1.5.1. Fourier and Laplace Transforms 1.5.2. Signal Statistics in the Frequency Domain 1.5.3. Input-Output Relationships of LTI Systems Summary Bibliography and Extended Reading Exploration Chapter 2: Sampling Quantization and Discrete Fourier 2.1. Sampling 2.1.1. Time Discretization 2.1.2. Aliasing 2.2. Fourier 2.3. Fourier Series of Periodic, Discrete-Time Signals 2.4. Practical FFTs 2.4.1. Positive and Negative Frequencies 2.4.2. Windowing 2.4.3. The Convolution Theorem 2.4.4. Avoiding Spectral Smearing—More Windows 2.5. Estimating Statistics Using Fourier Methods 2.5.1. Cross Power Spectral Density Function 2.5.2. Estimating the CPSD 2.6. Transfer Function Measurement in Noise 2.6.1. The Ordinary Coherence Function Summary Bibliography and Extended Reading Exploration Chapter 3: DSP in Acoustical Transfer Function Measurements 3.1. Acoustical Transfer Function Measurement Problems 3.2. Transfer Function Measurement Using MLS 3.2.1. Maximum Length Sequences (MLSs) 3.2.2. Some Useful Properties of MLS 3.2.3. Measure Once 3.2.4. No Truncation Errors 3.2.5. Crest Factor 3.3. Transfer Function Measurement Using Swept Sine Waves 3.3.1. Matched Filtering Summary Bibliography and Extended Reading Exploration and Mini Project Chapter 4: Digital Filters and z-Transform 4.1. General Introduction to Digital Filters 4.2. Finite Impulse Response (FIR) Filters 4.3. z-Transform and Transfer Function 4.4. Zero-Pole Plots 4.5. Infinite Impulse Response (IIR) Filters 4.6. Stability 4.7. Bilinear IIR Filters (BILINS) 4.8. Biquadratic IIR Filter Design (Biquads) 4.9. IIR Filter Design Using the Bilinear Transform 4.9.1. Butterworth Low Pass Filters 4.10. FIR Filter Design—The Fourier Transform Method 4.10.1. Time/Frequency Effects 4.10.2. Least Square Estimates of Transfer Functions 4.10.3. Practical Filters Have Real Coefficients 4.10.4. Zero Phase and Linear Phase Filters 4.10.5. Recapitulation: FIR Filter Design Procedure Summary Bibliography and Extended Reading Exploration Chapter 5: Audio Codecs 5.1. Audio Codecs 5.2. Quantization and PCM family encoding 5.2.1. Quantization as a Noise Source 5.2.2. Quantization as a Distortion Process 5.2.3. Dynamic Range due to Quantization 5.3. Dither 5.4. From PCM to DPCM 5.5. Oversampling and Low Bit Converters 5.6. One-Bit Conversion, Sigma-Delta Modulation 5.7. Lossy Codecs and MPEG Codecs Summary References Bibliography and Extended Reading Exploration and Mini Project Chapter 6: DSP in Binaural Hearing and Microphone Arrays 6.1. Head Related Transfer Function and Binaural Signal Processing 6.1.1. Head Related Transfer Functions (HRTFs) 6.1.2. HRTF Data 6.1.3. Application Scenarios 6.2. Microphone Arrays and Delay-Sum Beamformers Summary References Bibliography and Extended Reading Exploration Chapter 7: Adaptive Filters 7.1. General Model of LMS Adaptive Filters 7.2. Four Generic Types of Adaptive Filters 7.2.1. System Identification 7.2.2. Inverse Modelling 7.2.3. Noise or Interference Cancellation 7.2.4. Linear Prediction 7.3. From Optimal Filter to Least Mean Square (LMS) Adaptive Algorithms 7.3.1. Concept of Optimal Filters 7.3.2. A Discrete-Time Formulation of Optimal Filter 7.3.3. Adaptive Methods and LMS Algorithm 7.4. Genetic Algorithms: Another Adaptive Technique Genetic Algorithms 7.4.1. Genetic Algorithms Summary Reference Bibliography and Extended Reading Exploration Chapter 8: Machine Learning in Acoustic DSP 8.1. General Concept of Acoustic Pattern Recognition 8.2. Common Acoustic Features 8.2.1. Acoustic Features and Feature Spaces 8.2.1.1. Time-Domain Features 8.2.1.2. Frequency-Domain Features 8.2.2. Time-Frequency Domain 8.2.2.1. Mel-Frequency Cepstrum Coefficients 8.3. Decision Making by Machine Learning 8.3.1. Machine Learning 8.3.2. Artificial Neural Network 8.3.2.1. Neuron Models 8.3.3. Topology of Artificial Neural Network 8.3.4. Supervised Learning Rule 8.4. Training, Testing and Validation 8.4.1. Training and Testing 8.4.1.1. Holdout Cross-Validation 8.4.1.2. K-Fold Cross-Validation 8.4.2. Over-Fitting and Under-Fitting 8.4.3. Stop Criterion, Step Size, and Restart 8.5. Speech Recognition 8.6. Speaker Recognition 8.7. Music Information Retrieval 8.8. Machine Audition of Acoustics 8.8.1. Acoustic Transmission Channels and Acoustic Parameters 8.8.2. Extraction of Reverberation Time from Discrete Utterances 8.8.3. Estimation of Speech Transmission Index from Running Speech 8.8.4. Estimation of Reverberation Time from Running Speech 8.8.5. Using Music as Stimuli 8.9. Blind Estimation with a Parametric Model: Maximum Likelihood Estimation Summary References Bibliography and Extended Reading Recommended Software and Tool Kits Exploration and Mini Projects Chapter 9: Unsupervised Learning and Blind Source Separation 9.1. Hebbian Learning (Self-Organised Learning) 9.2. PCA Neural Networks 9.2.1. Hebbian Maximum Eigenfilter and PCA 9.2.2. Generalised Hebbian Algorithm and PCA Network 9.3. ICA Neural Networks and Blind Source Separation 9.4. Blind Estimation of Room Acoustic Parameters Using a PCA Network as a Feature Extractor Summary References Bibliography and Extended Reading Recommended Software and Tool Kits Exploration and Mini Project Chapter 10: DSP in Hearing Aids 10.1. Technical Challenges of Hearing Aids 10.2. Audiometry and Hearing Aid Fitting 10.3. Filter Bank and Multi-Band Compression 10.3.1. Filter Bank 10.3.2. Compression Channel 10.4. Acoustic Feedback Cancellation 10.5. Transposition and Frequency Lowering 10.6. Other Add-N Features Summary References Index