This page is a companion for MUS2640 but is probably helpful for university work in general. You do not need to read it all at once; dip back in when you are planning a text or coming up to an exam.
Academic writing¶
Good writing (academic and other) start by having a clear idea of what you want to write about. If it is an assignment, the task is already given (compare, explain, argue, reflect, etc.). If not, you will need to decide yourself. You should also note word limits, deadline, format, and the citation style before you invest time in drafting. If anything is unclear, ask rather than assume something.
Planning saves time. Sketch a few bullet points for your main line of argument, then distinguish description from analysis, which means separating what something is from why it matters in context. For each section, know what evidence you will lean on: research articles, listening examples, or some of your own data.
A clear essay usually has an introduction that describes the context, the question you answer, and a one- or two-sentence summary. Then, each body paragraph should be built around one idea starting with a topic sentence, then some evidence backing up your argument, and finally a bridge forward to the next paragraph. Think of writing as creating an onion. You start with a core and develop layers that support that argument. You should end with a conclusion that knits the various threads you have vowen together and connect to your introduction. You may briefly note limits of your argument or what further research would be needed.
For voice, third person often fits analysis (“The results suggest…”), while first person is natural for reflection or methods when the assignment invites it. Prefer active phrasing when it clarifies who did what, and avoid drifting between voices without reason.
Writing about music¶
Writing about sound and music poses some challenges with connecting to the material you are describing. You can describe the sound with words and you can include visualisations (such as a spectrogram). It often also helps to link to a recording, but then be careful with describing the location you are describing (“at 1:42 in the Coltrane recording”), or bar numbers against a named edition if you are working from a score. “Later in the piece” is not an address.
Then describe before you interpret. Say what happens in terms someone could verify by listening, and only then say what you think it means. “The saxophone enters an octave above the previous phrase, and the bass drops out for four bars, which leaves the melody exposed at the point where the text turns” earns its conclusion. “The music becomes emotional here” does not, because there is nothing in it to check.
Keep your terminology fixed for the length of a text. Decide whether you are naming pitches by letter (C4), by solfège, or by scale degree, and stay with it. Decide whether “tempo” in your essay means the notated marking or the measured rate, and say which. If you use a term this book defines, such as timbre, metre, or masking, use it in that sense rather than the loose everyday one, and if you need the everyday sense, flag it.
When you are working from your own analysis, whether that is a spectrogram, a tempo curve, or a set of annotations, describe how you produced it. Which recording, which software, which settings. An analysis nobody can reproduce is an opinion with a figure attached.
Remember to cite anything that is not common knowledge, including paraphrases, quotations, and figures.
When you revise, use separate passes so you do not try to fix everything at once:
- Structure — does the argument flow from paragraph to paragraph?
- Claims and evidence — is each main point supported?
- Language and references — clarity, tone, and accurate citation.
Remember to format the text so that it looks coherent and is easy to follow. Use consistent font sizes, heading levels, indents, and spacings. Deliver nice-looking PDFs if you are not asked for something else.
Figures and plots¶
A figure should be readable on its own. Whether you make it yourself or borrow it, check that a reader could understand it without digging through your prose.
- Caption — Every figure needs a caption that says what the reader is looking at and what to notice, not just a repeated title. A good caption names the data and points to the takeaway (for example, “Spectrogram of a saxophone tone; note the evenly spaced harmonics”).
- Axes — Label both axes and give the units (Hz, dB, seconds, BPM). An unlabelled axis makes a plot almost meaningless.
- Legend and scale — If you show more than one series, add a legend. Explain tick values and any colour scale, and do not rely on colour alone to carry meaning.
- Readability — Use font sizes large enough to read at the size the figure will actually appear, and keep the design uncluttered.
- Reference and credit — Refer to each figure in the text (“Figure 3 shows…”) so the reader knows when to look, and cite the source if the figure is not your own.
The plots produced by the code examples in this book aim to follow these habits: labelled axes with units, and a caption that explains the content.
Referencing and source use¶
Avoid too many direct quotes in your writing. It is better to show that you can reformulate other people’s thinking in your own words. Use page numbers when you cite or paraphrase concrete things in other people’s texts.
There is no particular reference style asked for in this course. The most important is that you are consistent. Choose one (e.g., APA, Chicago, or Harvard) and stick to it. Use a reference manager (for example, Zotero) to keep track of your references. That will save you a lot of time later. In addition to helping with your references, it can serve as the start of your digital research library.
Use relevant databases for finding literature. Google Scholar is often the first place to start looking, but the university library is a better place to go for getting access to material behind paywalls.
Study technique¶
Most people learn more from short focused blocks (~30 minutes) and real breaks than from staring passively at the same page for hours. When you need depth, lean on a calm environment and leave digital distractions aside.
After reading a chapter, try one or more of these:
- Close the notes and write half a page in your own words.
- Explain a concept aloud as if to someone new to the topic.
- Turn each section heading into a question and answer it before you reread.
Since this course is all about listening and experiencing your sonic environments, turn on your senses in everyday life. Try to apply what you learn to what you experience.
Test out AI tools, if you like. Feel free to explore tools such as NotebookLM with this textbook to pose test questions.
Exam preparation¶
This course uses a school exam without any tools. That means that you will need to express yourself without any tools. The questions will not be made to test your memory, but to check whether you have understood the main concepts and how things relate.
The best preparation for the exam is to read through this textbook, show up in class, and do the assignments.
Also practice your general academic writing. This includes sharpening your language, including the use of key terms (e.g., differences between “sound” and “audio”).
During the exam, remember to answer the questions posed and set aside enough time to get through all the questions. If a question freezes you, begin with what you do know and return later. Partial answers usually earn more than blanks.
How to read a spectrogram¶
A spectrogram is a picture of sound. Time runs along the horizontal axis, frequency runs up the vertical axis, and brightness shows how much energy the sound carries at each point. A bright patch means strong energy at that frequency and that moment, while a dark patch means little or none. This page collects the essentials for reading such pictures, a skill you will use throughout the course.
What common sounds look like¶
A harmonic sound, such as a sung vowel or a plucked guitar string, appears as a stack of horizontal lines. The lowest line is the fundamental frequency, which normally matches the pitch you hear, and the lines above it are harmonics at whole-number multiples of the fundamental.
A drum hit or click appears as a thin vertical stripe. The event is short in time but spreads its energy across many frequencies at once, so it paints the whole height of the image for an instant.
A voice or instrument with vibrato shows the familiar harmonic stack, but each line waves gently up and down. The waves trace the periodic pitch variation, and they are easiest to spot in the upper harmonics, where the movement is magnified.
Noise, such as a cymbal wash, wind, or hiss, appears as a diffuse wash of energy without clear lines, because the energy is spread continuously across the frequency range.
Silence appears as darkness. In practice a recording is rarely completely dark, since background noise usually leaves a faint glow near the bottom of the image.
The figure below shows all of these in a single synthesised clip.
Source
# Annotated spectrogram of a synthesised clip with a count-in click,
# a harmonic tone with vibrato, a noise burst, and silence
import numpy as np
import matplotlib.pyplot as plt
import librosa
import librosa.display
rng = np.random.default_rng(2640)
sr = 22050
duration = 6.0
n = int(sr * duration)
t = np.linspace(0, duration, n, endpoint=False)
y = np.zeros(n)
# Count-in clicks: two short broadband bursts
for start in (0.4, 0.9):
idx = (t >= start) & (t < start + 0.012)
y[idx] += 0.9 * rng.uniform(-1, 1, idx.sum())
# Sustained harmonic tone with vibrato (f0 = 220 Hz, 5.5 Hz vibrato)
f0, vib_rate, vib_depth = 220.0, 5.5, 8.0
inst_f0 = f0 + vib_depth * np.sin(2 * np.pi * vib_rate * t)
phase0 = 2 * np.pi * np.cumsum(inst_f0) / sr
tone = np.zeros(n)
for k in range(1, 7):
tone += (0.5 / k) * np.sin(k * phase0)
env = np.where((t >= 1.5) & (t < 3.8), 1.0, 0.0)
ramp = int(0.05 * sr)
attack = np.searchsorted(t, 1.5)
release = np.searchsorted(t, 3.8)
env[attack:attack + ramp] *= np.linspace(0, 1, ramp)
env[release - ramp:release] *= np.linspace(1, 0, ramp)
y += 0.6 * tone * env
# Noise burst
idx = (t >= 4.2) & (t < 4.8)
y[idx] += 0.35 * rng.uniform(-1, 1, idx.sum())
# Silence from 4.8 s onwards (nothing added)
y = y / np.max(np.abs(y))
n_fft, hop_length = 2048, 256
D = librosa.stft(y, n_fft=n_fft, hop_length=hop_length, window='hann')
S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max)
fig, ax = plt.subplots(figsize=(12, 5), constrained_layout=True)
img = librosa.display.specshow(S_db, sr=sr, hop_length=hop_length,
x_axis='time', y_axis='hz', cmap='magma', ax=ax)
ax.set_ylim(0, 2500)
ax.set_title('An annotated spectrogram of a six-second synthesised clip')
fig.colorbar(img, ax=ax, format='%+2.0f dB')
notes = [
('count-in clicks:\nvertical stripes', (0.65, 1900), (1.15, 2300)),
('harmonics:\nhorizontal stack', (2.6, 480), (2.05, 2050)),
('vibrato:\nwavy lines', (3.3, 1310), (3.85, 1850)),
('noise burst:\nbroadband wash', (4.5, 1500), (4.35, 2250)),
('silence:\ndarkness', (5.5, 900), (5.15, 1500)),
]
for text, xy, xytext in notes:
ax.annotate(text, xy=xy, xytext=xytext, color='white', fontsize=10,
ha='center', va='center',
arrowprops=dict(arrowstyle='->', color='white', lw=1.2))
plt.show()
The window-size trade-off¶
Every spectrogram is computed with an analysis window, and the window size sets a trade-off: a short window is sharp in time but blurry in frequency, while a long window is sharp in frequency but blurry in time. The acoustics chapter explains this trade-off in more detail and shows the same signal analysed with three window sizes.
Checklist when describing a spectrogram¶
- Start with the axes: note the time span, the frequency range, and whether the frequency and intensity scales are linear or logarithmic.
- Identify the obvious events: where does something clearly begin, end, or change?
- Sort the content into harmonic (horizontal lines), percussive (vertical stripes), and noisy (diffuse washes).
- Describe how the sound develops over time: what stays constant and what moves?
- State what you cannot conclude: a spectrogram shows physical energy, not perceived loudness, and it does not by itself reveal which instrument or source produced the sound.