diff --git a/boat_tutorials/decibel.ipynb b/boat_tutorials/decibel.ipynb new file mode 100644 index 0000000..a81a3e3 --- /dev/null +++ b/boat_tutorials/decibel.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Decibel\n", + "\n", + "Sound levels are often described on a decibel scale. But what is actually a decibel, and how does it relate to the signals we are measuring?" + ], + "metadata": { + "id": "6FEfP7LsP7gt" + }, + "id": "6FEfP7LsP7gt" + }, + { + "cell_type": "markdown", + "source": [ + "Decibel is a relative measure of the ratio of power of two signals on a logarithmic scale. Yet, we often hear noise levels announced in terms of decibels and we have an understanding of what those mean in an \"absolute\" sense. The hidden reference for sounds in air is $20\\mu Pa$, which corresponds to the threshold below which a human usually cannot hear (thus making that sound's level equal to 0dB). In water, however, a difference reference value: $1 \\mu Pa$." + ], + "metadata": { + "id": "dm_pdPXBUqbU" + }, + "id": "dm_pdPXBUqbU" + }, + { + "cell_type": "markdown", + "source": [ + "The decibel level of a signal with power $P_1$ with respect to a reference signal with power $P_0$ is\n", + "\n", + "$$10 \\log_{10}(P_1/P_0) dB $$.\n", + "\n", + "We can see that decibel is a scaled measure of the difference in the logarithm of the power of the signals." + ], + "metadata": { + "id": "yO7rX_ObgaTY" + }, + "id": "yO7rX_ObgaTY" + }, + { + "cell_type": "markdown", + "source": [ + "### **Exercise 1: Comparing Power**\n", + "\n", + "Look at the table the decibel levels for breathing and trumpet. How much louder is the trumpet than the breathing?\n", + "\n", + "\n", + "breathing: 10 dB\n", + "\n", + "trumpet: 110 dB\n", + "\n", + "$100dB = 10\\log_{10}\\left(\\frac{P_1}{P_2}\\right)$\n", + "\n", + "The trumpet's power is 10^10 higher than that of breathing.\n" + ], + "metadata": { + "id": "id3xe2CLkqf6" + }, + "id": "id3xe2CLkqf6" + }, + { + "cell_type": "markdown", + "source": [ + "### **Exercise 2: dB Scaling**\n", + "\n", + "Let's consider two scenarios:\n", + "\n", + "1. 1 person talking\n", + "\n", + "2. 100 people talking\n", + "\n", + "What is $dB$ level of signal 2 with respect to signal 1?\n", + "\n", + "$10\\log_{10}(100)=20 dB$\n" + ], + "metadata": { + "id": "IbrPLgKPlZDO" + }, + "id": "IbrPLgKPlZDO" + }, + { + "cell_type": "markdown", + "source": [ + "### **Exercise 3: Air vs Water dB**\n", + "\n", + "What is the difference between air and water $dB$ values?\n", + "\n", + "$20\\log_{10}(20) = 26 dB$" + ], + "metadata": { + "id": "b-JxMbj5oQHt" + }, + "id": "b-JxMbj5oQHt" + }, + { + "cell_type": "markdown", + "source": [ + "## Ship vs Background Noise Level Example" + ], + "metadata": { + "id": "Hb7CSJBOpq01" + }, + "id": "Hb7CSJBOpq01" + }, + { + "cell_type": "markdown", + "id": "74e852d7-9edb-44b9-8c68-a359fc3fecd1", + "metadata": { + "id": "74e852d7-9edb-44b9-8c68-a359fc3fecd1" + }, + "source": [ + "We will calculate the dB of a ship signal in the ocean with reference to the background sound when there is no ship passing by. The data is obtained from the [Orcasound hydrophone](https://live.orcasound.net/listen/orcasound-lab) on San Juan Island, WA." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8172aca-bfb2-498e-8610-0917a9ad0aa1", + "metadata": { + "id": "e8172aca-bfb2-498e-8610-0917a9ad0aa1" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np" + ], + "metadata": { + "id": "peP4pNd4pgX3" + }, + "id": "peP4pNd4pgX3", + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from scipy.io import wavfile\n" + ], + "metadata": { + "id": "ae_WiTj1oIAF" + }, + "id": "ae_WiTj1oIAF", + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "First we read the signals using the `wavfile` function from the `scipy.io` module." + ], + "metadata": { + "id": "_TtuDjz7Iu_z" + }, + "id": "_TtuDjz7Iu_z" + }, + { + "cell_type": "code", + "source": [ + "# reading ship data\n", + "ship_samplerate, ship_signal = wavfile.read('ship.wav')" + ], + "metadata": { + "id": "CEmlVOrJRHY6" + }, + "id": "CEmlVOrJRHY6", + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# reading background data\n", + "bg_samplerate, bg_signal = wavfile.read('background.wav')" + ], + "metadata": { + "id": "Gipk8NDSRXoV" + }, + "id": "Gipk8NDSRXoV", + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "737aec53-bb62-4b0d-83ae-f63c4fb07073", + "metadata": { + "id": "737aec53-bb62-4b0d-83ae-f63c4fb07073" + }, + "source": [ + "We will display the amplitute of the signals." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bd124bd3-b61f-4108-b63f-30d21b327c32", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "bd124bd3-b61f-4108-b63f-30d21b327c32", + "outputId": "925ed44e-6a98-44e2-fb94-80bea39fe586" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time in seconds')" + ] + }, + "metadata": {}, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(np.linspace(0, len(ship_signal)/ship_samplerate, len(ship_signal)), ship_signal)\n", + "plt.title(\"Ship Signal Amplitute\")\n", + "plt.xlabel(\"Time in seconds\")" + ] + }, + { + "cell_type": "code", + "source": [ + "plt.plot(np.linspace(0, len(bg_signal)/bg_samplerate, len(bg_signal)), bg_signal)\n", + "plt.title(\"Background Signal Amplitute\")\n", + "plt.xlabel(\"Time in seconds\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "v-SOfw7fR_VA", + "outputId": "e49f1560-84f2-490c-88ad-4061bb708fb3" + }, + "id": "v-SOfw7fR_VA", + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time in seconds')" + ] + }, + "metadata": {}, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We observe that the ranges of amplitude for the two signals are very different: while the background signal ranges $~(-1000,1000)$, the ship signal ranges $(-10000,10000)$." + ], + "metadata": { + "id": "xngUlrxlyMKz" + }, + "id": "xngUlrxlyMKz" + }, + { + "cell_type": "markdown", + "source": [ + "### Root Mean Squares\n", + "\n" + ], + "metadata": { + "id": "JPCdNao2w4xB" + }, + "id": "JPCdNao2w4xB" + }, + { + "cell_type": "markdown", + "source": [ + "The power of the signal can be obtained by calculating the root mean squares of the signal's amplitude $s$ in the time domain:\n", + "\n", + "$p(s) = RMS(s) = \\sqrt{\\frac{1}{N}\\sum_{i=1}^N s_i^2}$,\n", + "\n", + "where $N$ is the number of samples." + ], + "metadata": { + "id": "xe28r7RCFyzO" + }, + "id": "xe28r7RCFyzO" + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "54c76e32-8823-4c82-8318-b719e87923bb", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "54c76e32-8823-4c82-8318-b719e87923bb", + "outputId": "d0bde930-af00-4035-fc35-c9e3893bffdc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The root mean square of the ship signal is: 2019.99\n" + ] + } + ], + "source": [ + "# rms for ship signal\n", + "rms_ship = np.sqrt(np.mean(np.square(ship_signal.astype('float'))))\n", + "\n", + "print(\"The root mean square of the ship signal is: \" + str(round(rms_ship,2)))" + ] + }, + { + "cell_type": "code", + "source": [ + "# rms for background signal\n", + "rms_bg = np.sqrt(np.mean(np.square(bg_signal.astype('float'))))\n", + "\n", + "print(\"The root mean square of the background signal is: \" + str(round(rms_bg,2)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KPugxvyxMClH", + "outputId": "0678b570-0328-47d7-f7a0-15ca5c56b974" + }, + "id": "KPugxvyxMClH", + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The root mean square of the background signal is: 135.58\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Warning!** We converted the signal to `float` to avoid integer overflow. Try to do the calculation with integers, and notice the difference." + ], + "metadata": { + "id": "-6TtJJIV7Q6i" + }, + "id": "-6TtJJIV7Q6i" + }, + { + "cell_type": "markdown", + "source": [ + "### Decibel Computation" + ], + "metadata": { + "id": "ktAV75vZPtJr" + }, + "id": "ktAV75vZPtJr" + }, + { + "cell_type": "markdown", + "source": [ + "The difference in the levels of the two signals is provided in decibels:\n", + "\n", + "$10\\log_{10}(P_{\\mathrm{ship}}/ P_{\\mathrm{bg}}) = 20*\\log_{10}(rms_{\\mathrm{ship}}/rms_{\\mathrm{bg}})$" + ], + "metadata": { + "id": "Fg1M7154Pwyj" + }, + "id": "Fg1M7154Pwyj" + }, + { + "cell_type": "code", + "source": [ + "20 * np.log10(rms_ship/rms_bg)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xSmkJYGOPwBY", + "outputId": "fdf6eda9-faa9-4c89-d515-8d39dd0328f8" + }, + "id": "xSmkJYGOPwBY", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "23.462827095013377" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "23 dB difference corresponds to ~15 ratio in rms, or ~225 ratio in power between the too signals." + ], + "metadata": { + "id": "pbOviNU6tTAb" + }, + "id": "pbOviNU6tTAb" + }, + { + "cell_type": "markdown", + "source": [ + "### RMS with `librosa`" + ], + "metadata": { + "id": "Vl8CJO8Fxjht" + }, + "id": "Vl8CJO8Fxjht" + }, + { + "cell_type": "markdown", + "source": [ + "Root mean square can also be obtained through the built-in function `feature.rms` in the `librosa` library. The function is designed to calculate the power over fixed (potentially overlapping) intervals. However, in this scenario we would like to calculate it for the whole signal so we set the window length to be equal to the length of the signal and the hopping interval to be the same so there is only one interval." + ], + "metadata": { + "id": "HkOjn6c-MDlA" + }, + "id": "HkOjn6c-MDlA" + }, + { + "cell_type": "code", + "source": [ + "#!pip install librosa" + ], + "metadata": { + "id": "iF6u8DqBnl0x" + }, + "id": "iF6u8DqBnl0x", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import librosa" + ], + "metadata": { + "id": "6hcpmbuQLX7B" + }, + "id": "6hcpmbuQLX7B", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "rms_bg = librosa.feature.rms(y=bg_signal, frame_length=len(bg_signal), hop_length=len(bg_signal), center=False)" + ], + "metadata": { + "id": "7U5Ue8khJAhs" + }, + "id": "7U5Ue8khJAhs", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "rms_bg" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8H5t3iwjucTE", + "outputId": "3ee3c544-88ab-4d24-d86c-74733e1f553e" + }, + "id": "8H5t3iwjucTE", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[135.58351]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 33 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## References" + ], + "metadata": { + "id": "CVhV540oJHF2" + }, + "id": "CVhV540oJHF2" + }, + { + "cell_type": "markdown", + "source": [ + "* [What is dB? A Complete Guide for Measuring Sound using Decibel](https://www.hollyland.com/blog/tips/what-is-db)\n", + "* Svantek Academy: [Sound Pressure Level](https://svantek.com/academy/sound-pressure-level-spl/)\n", + "* Discovery of the Sounds of Sea: [Introduction to Decibels](https://dosits.org/science/advanced-topics/introduction-to-decibels/)" + ], + "metadata": { + "id": "EeDODJmTKMNd" + }, + "id": "EeDODJmTKMNd" + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "qwwdd277KLFX" + }, + "id": "qwwdd277KLFX", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/boat_tutorials/decibel.md b/boat_tutorials/decibel.md index 77bcbc1..f689a8f 100644 --- a/boat_tutorials/decibel.md +++ b/boat_tutorials/decibel.md @@ -1,3 +1,113 @@ # Decibel -Help! \ No newline at end of file + +## What are we measuring? + +* Start with an example of sound wave plot. What units is the data actually in? +* discussion of units + + + +### Definition of Decibel +* Decibel is a relative measure of the ratio in power of two signals on a log-scale. Alternatively, one can think of it as the difference of log of the + +$10 \log(s_2/s_1)$ + +* read several signals and calculate the decibel with one referenced to the other one +* discuss general reference level + + +* Difference between decibel in air and ocean + * air: human hearing + * ocean:?? + + + 20 + + + pressure *velocity constant + + signal voltage to pressure + + gain voltage + + + power spectrum )if it is calibrated + +### Decibels in Air vs Water + +While in air the reference level is the $20\mu Pa$ (the minimum level a human can hear), the reference in water is $\mu Pa$. Calculate what is the difference between dB in water and air. + +$20\log_{10}(20) = 26 dB$ + +## What are we hearing? + +* example of pretty plot with examples + +## What do animals hear? + +* Point to how the range is high and the sounds of interest + + + + +## Exercises + +**Exercise 1:** Look at the table the decibel levels for breathing and trumpet. How much louder is the trumpet than the breathing? + + +breathing: 10 dB + +trumpet: 110 dB + +$100dB = 10\log_{10}\left(\frac{P_1}{P_2}\right)$ + +The trumpet is 10^10 louder than the breathing. + + + +**Exercise 2:** + + 1. 1 person talking + 2. 10 people talking + +What is the dB level of signal 2 with respect to signal 1. + +$10\log_{10}(10)$ + + +**Exercise 3:** +How much louder is the ferry vs ambient noise? + + + +root mean square +average per second + +cw + + + + + + + + + + + +## Decibel References +* [What is dB? A Complete Guide for Measuring Sound using Decibel](https://www.hollyland.com/blog/tips/what-is-db) +* [Sound Pressure Level](https://svantek.com/academy/sound-pressure-level-spl/) + + + +## General References + +* [Understanding Sound](https://pressbooks.pub/sound) +* [Introduction to Speech Processing](https://speechprocessingbook.aalto.fi/) + + + + +