Audio AI Researcher & Developer

Model a sound wave mathematically using its properties of amplitude, frequency, and phase.

Explain the Nyquist-Shannon sampling theorem, derive its formula, and demonstrate the effect of aliasing.

Describe the process of quantization and the role of bit depth in determining audio fidelity.

Compare and contrast common digital audio formats (WAV, FLAC, MP3) and channel layouts (mono, stereo).

Perform audio format conversion, resampling, and channel manipulation using ffmpeg commands.

Load, decode, and represent audio tensors in PyTorch using torchaudio's I/O backends.

Visualize and interpret audio waveforms and perform basic time-domain operations like trimming and concatenation.

Apply various audio normalization techniques (peak, RMS, LUFS) and explain their use cases.

Define the Continuous Fourier Transform and its role in decomposing signals into frequency components.

Derive the Discrete Fourier Transform (DFT) as the discrete-time counterpart of the Fourier Transform.

Implement a DFT from scratch and interpret its magnitude and phase spectra outputs.

Explain the computational efficiency of the Fast Fourier Transform (FFT) algorithm compared to a naive DFT.

Derive the Short-Time Fourier Transform (STFT) and explain the trade-offs of different windowing functions.

Compute and visualize spectrograms from audio signals using Python libraries like librosa or torchaudio.

Explain the psychoacoustic basis of the mel scale and convert linear spectrograms to mel-spectrograms.

Derive Mel-Frequency Cepstral Coefficients (MFCCs) from mel-spectrograms using the Discrete Cosine Transform (DCT).

Apply spectral gating and statistical Wiener filtering to denoise speech signals.

Perform voice activity detection (VAD) to segment speech from non-speech regions in an audio stream.

Implement time-domain audio augmentation techniques, including speed perturbation and pitch shifting.

Implement frequency-domain audio augmentation by applying SpecAugment to mel-spectrograms.

Design and build a robust audio data loading and batching pipeline in PyTorch using torchaudio.

Integrate on-the-fly feature extraction (e.g., mel-spectrogram generation) into a PyTorch data pipeline.

Explain the importance of forced alignment and apply a pre-trained model to generate word-level timestamps.

Implement and interpret standard ASR evaluation metrics, specifically Word Error Rate (WER) and Character Error Rate (CER).

Explain how 1D Convolutional Neural Networks (CNNs) can act as feature extractors for raw audio waveforms.

Describe the architecture of Recurrent Neural Networks (RNNs) and LSTMs for modeling temporal sequences.

Explain the self-attention mechanism and its advantages over recurrent models for capturing long-range dependencies.

Describe the complete Transformer architecture, including positional encoding, multi-head attention, and feed-forward layers.

Implement a multi-head self-attention layer in PyTorch, including support for causal masking.

Assemble a Transformer encoder block combining multi-head attention and a feed-forward network with residual connections.

Assemble a Transformer decoder block, including masked self-attention, cross-attention, and a feed-forward network.

Construct a full encoder-decoder Transformer model in PyTorch for sequence-to-sequence tasks.

Explain the core principles of Generative Adversarial Networks (GANs), including the generator, discriminator, and adversarial loss.

Describe the architectural components and training objective of a Deep Convolutional GAN (DCGAN).

Explain the architecture and objective function of a Variational Autoencoder (VAE), including the reparameterization trick.

Distinguish between the latent spaces learned by GANs and VAEs.

Describe the concept of Normalizing Flows for constructing complex probability distributions from simple ones.

Explain how invertible transformations are used in Normalizing Flow models.

Compare and contrast the characteristics of GANs, VAEs, and Flow-based models in terms of sample quality, diversity, and training stability.

Formulate speech recognition as a sequence-to-sequence problem and explain the alignment challenge.

Derive the Connectionist Temporal Classification (CTC) loss function and its forward-backward algorithm.

Implement greedy and beam-search decoding algorithms for a CTC output probability matrix.

Describe the Listen-Attend-Spell (LAS) architecture as an example of an attention-based encoder-decoder ASR model.

Describe the Whisper model architecture and its multitask, multilingual training strategy.

Fine-tune a pretrained Whisper model on a custom speech dataset using the Hugging Face ecosystem.

Explain how a language model can be integrated with a CTC-based acoustic model using shallow fusion.

Compare the trade-offs between CTC, attention-based, and hybrid ASR systems.

Explain the motivation for self-supervised pretraining in speech processing to leverage unlabeled data.

Describe the wav2vec 2.0 architecture, detailing its CNN feature encoder and Transformer context network.

Explain the wav2vec 2.0 pretraining objective, including the role of the quantization module and contrastive loss.

Describe the HuBERT architecture and its offline clustering-based pretraining objective (masked prediction of acoustic units).

Fine-tune a pretrained wav2vec 2.0 model for ASR and compare its performance to a model trained from scratch.

Explain how self-supervised speech models enable transfer learning across different languages and downstream tasks.

Explore and analyze the learned representations from a self-supervised speech model.

Describe the standard TTS pipeline: text normalization, grapheme-to-phoneme conversion, acoustic model, and vocoder.

Describe the Tacotron 2 architecture, focusing on its encoder, location-sensitive attention, and autoregressive decoder.

Describe the FastSpeech 2 architecture, highlighting its non-autoregressive design and variance adaptor module.

Compare autoregressive vs. non-autoregressive TTS models in terms of synthesis quality, speed, and controllability.

Describe autoregressive neural vocoders like WaveNet and WaveRNN, focusing on their use of dilated causal convolutions.

Describe the HiFi-GAN vocoder, including its generator and multi-scale/multi-period discriminators.

Generate an audio waveform from a mel-spectrogram using a pretrained HiFi-GAN model.

Train a complete TTS system (e.g., FastSpeech 2 + HiFi-GAN) using an ESPnet2 recipe.

Describe the VITS architecture, explaining how it integrates a VAE, normalizing flows, and a GAN-based decoder for end-to-end training.

Explain how speaker embeddings are used to condition a TTS model for multi-speaker speech synthesis.

Describe the methodology behind zero-shot voice cloning using a separately trained speaker encoder network.

Prepare a custom dataset for voice cloning, including audio segmentation, cleaning, and transcription.

Configure and launch a fine-tuning job for a multi-speaker TTS model (e.g., VITS) using Coqui TTS.

Synthesize speech using the fine-tuned model for both seen and unseen speakers (zero-shot).

Evaluate synthesized speech quality using objective (e.g., PESQ) and subjective (e.g., Mean Opinion Score) metrics.

Analyze the impact of dataset size and quality on voice cloning performance.

Explain how neural audio codecs like SoundStream or EnCodec discretize audio waveforms into a sequence of tokens.

Describe how a standard language model architecture (e.g., decoder-only Transformer) can be applied to discrete audio tokens for generation (AudioLM).

Explain the mechanics of in-context learning for audio generation in models like VALL-E.

Describe cascaded vs. end-to-end systems for Speech-to-Speech Translation (S2ST) and analyze their trade-offs.

Explain the architecture of a direct S2ST model, such as Meta's SeamlessM4T.

Run a speech-to-speech translation task using a pre-built recipe from a toolkit like Fairseq or ESPnet-ST.

Describe the architecture of multimodal audio-text LLMs like AudioPaLM.

Discuss the challenges and future directions in audio language modeling, including long-form generation and expressiveness.

Explain the principles of knowledge distillation and apply it to compress a large speech model into a smaller student model.

Apply post-training dynamic quantization and static quantization to a speech model.

Evaluate the trade-off between model size, inference speed, and performance degradation after quantization.

Export a trained PyTorch speech model to ONNX format for framework-agnostic deployment.

Perform inference with the exported model using ONNX Runtime and benchmark its performance.

Describe the architectural requirements for streaming ASR systems and their latency-accuracy trade-offs.

Implement a basic streaming inference loop for a frame-synchronous ASR model.

Analyze the performance bottlenecks in a typical speech AI inference pipeline.

Design a REST API for a speech service (e.g., ASR or TTS) using FastAPI, defining request/response models.

Build a functional API endpoint that encapsulates a trained speech model for inference.

Containerize the speech application and its dependencies using Docker.

Manage model artifacts and data versions for reproducibility using a tool like DVC.

Design the system architecture for a conversational AI agent combining STT and TTS components in a pipeline.

Propose a deployment strategy for the conversational agent, considering scaling, container orchestration, and cloud services.

Identify and discuss current research frontiers in audio AI, such as low-resource languages, expressive synthesis, and robust recognition.

Formulate a research proposal for a novel problem in audio AI, outlining the methodology and evaluation plan.