Audio Stream - Process and Buffer a Real-Time Audio Data Stream
Question Details
Round 1 Coding
Problem
You are building an audio processing pipeline. Audio arrives as a continuous stream of integer samples. Implement an AudioBuffer that:
1. Accepts incoming samples one at a time.
2. When the internal buffer reaches a fixed frame_size, processes the frame (apply a simple gain factor) and emits it.
3. Allows querying how many complete frames have been emitted.
python
class AudioBuffer:
def __init__(self, frame_size: int, gain: float):
pass
def write(self,
**sample**: int) -> list[int] | None:
**Returns** the processed frame when buffer is full; None otherwise
pass
def frames_emitted(self) -> int:
pass
def flush(self) -> list[int] | None:
# Emit a partial frame padded with zeros if any samples remain
pass
Example
buf = AudioBuffer(frame_size=4, gain=2.0)
buf.write(10) # -> None
buf.write(20) # -> None
buf.write(30) # -> None
buf.write(40) # -> [20, 40, 60, 80] (gain applied)
buf.frames_emitted() # -> 1
buf.write(50)
buf.flush() # -> [100, 0, 0, 0]
Follow-ups
- How do you handle overflow when gain causes sample values to exceed int16 range?
- How would you implement a sliding window (overlapping frames) instead of non-overlapping?
- If samples arrive from multiple concurrent threads, how do you make
writethread-safe? - How do you design this for a real-time system where dropped frames cause audible glitches?
Full Details
Round 1 Coding
Problem
You are building an audio processing pipeline. Audio arrives as a continuous stream of integer samples. Implement an AudioBuffer that:
1. Accepts incoming samples one at a time.
2. When the internal buffer reaches a fixed frame_size, processes the frame (apply a simple gain factor) and emits it.
3. Allows querying how many complete frames have been emitted.
python
class AudioBuffer:
def __init__(self, frame_size: int, gain: float):
pass
def write(self,
**sample**: int) -> list[int] | None:
**Returns** the processed frame when buffer is full; None otherwise
pass
def frames_emitted(self) -> int:
pass
def flush(self) -> list[int] | None:
# Emit a partial frame padded with zeros if any samples remain
pass
Example
buf = AudioBuffer(frame_size=4, gain=2.0)
buf.write(10) # -> None
buf.write(20) # -> None
buf.write(30) # -> None
buf.write(40) # -> [20, 40, 60, 80] (gain applied)
buf.frames_emitted() # -> 1
buf.write(50)
buf.flush() # -> [100, 0, 0, 0]
Follow-ups
- How do you handle overflow when gain causes sample values to exceed int16 range?
- How would you implement a sliding window (overlapping frames) instead of non-overlapping?
- If samples arrive from multiple concurrent threads, how do you make
writethread-safe? - How do you design this for a real-time system where dropped frames cause audible glitches?
About This Question
This is a reported interview question from a toma interview during the phone round.
It covers the following topics: Coding, Phone, Sliding Window .
Topics
About Toma Interview Reports
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How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Toma reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Toma Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Toma reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.