feat : things

+import pyaudio
+import numpy as np
+
+# Define constants
+CHUNK_SIZE = 1024  # Number of audio frames per buffer
+FORMAT = pyaudio.paInt16  # Audio format (16-bit int)
+CHANNELS = 1  # Mono audio
+RATE = 44100  # Sample rate (Hz)
+RMS_REF = 1.0  # Reference RMS amplitude for dB calculation
+
+# Initialize PyAudio stream
+audio = pyaudio.PyAudio()
+stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK_SIZE)
+
+# Main loop
+while True:
+    # Read audio data from stream
+    data = stream.read(CHUNK_SIZE)
+    
+    # Convert audio data to numpy array
+    audio_array = np.frombuffer(data, dtype=np.int16)
+    
+    # Calculate RMS amplitude
+    rms = np.sqrt(np.mean(np.square(audio_array)))
+    
+    # Calculate dB level
+    avg_db = 20 * np.log10(rms / RMS_REF)
+    max_db = 20 * np.log10(np.max(audio_array) / RMS_REF)
+    
+    # Print dB level to console
+    try:
+        if(avg_db != np.NaN and max_db != np.NaN and max_db > 50):
+            print("dB level:", int(avg_db), int(max_db))
+    except:
+        continue
\ No newline at end of file

-"""PyAudio Example: Audio wire between input and output. Callback version."""
-
-import time
-import sys
-import pyaudio
-import numpy as np
-
-
-DURATION = 5  # seconds
-
-delay_buffer = np.zeros((44100, 2), dtype=np.float32)
-
-def callback(in_data, frame_count, time_info, status):
-    global delay_buffer
-    audio_data = np.frombuffer(in_data, dtype=np.float32).reshape(frame_count, 2)
-    delayed_data = np.concatenate((delay_buffer, audio_data))
-    delay_buffer = delayed_data[frame_count:]
-    return (audio_data + 0.5 * delay_buffer).tobytes(), pyaudio.paContinue
-
-p = pyaudio.PyAudio()
-stream = p.open(format=p.get_format_from_width(2),
-                channels=1,
-                rate=44100,
-                input=True,
-                output=True,
-                frames_per_buffer=1024,
-
-                stream_callback=callback)
-
-start = time.time()
-while stream.is_active() and (time.time() - start) < DURATION:
-    time.sleep(0.1)
-
-stream.close()
-p.terminate()
\ No newline at end of file

+import pyaudio
+import numpy as np
+import scipy.signal as signal
+import matplotlib.pyplot as plt
+# 파라미터 설정
+RATE = 44100  # 샘플링 주파수
+CHUNK = 1024  # 읽을 샘플의 수
+THRESHOLD = 256  # 피크를 검출하기 위한 threshold 값
+WIN_SIZE = 1024  # STFT를 적용할 윈도우 사이즈
+HOP_SIZE = 512  # STFT에서 윈도우 사이의 거리 (오버랩 사이즈)
+
+# PyAudio 객체 생성
+p = pyaudio.PyAudio()
+
+# 콜백 함수 정의
+def process_audio(in_data, frame_count, time_info, status):
+    # 오디오 데이터 변환
+    data = np.frombuffer(in_data, dtype=np.int16)
+
+    # STFT 수행
+    f, t, Zxx = signal.stft(data, RATE, nperseg=WIN_SIZE, noverlap=HOP_SIZE)
+
+    # 피크 검출
+    peaks, _ = signal.find_peaks(np.abs(np.mean(Zxx, axis=1)), height=THRESHOLD, distance=WIN_SIZE)
+    # 파라미터 추정
+    if len(peaks) > 0:
+        peak_idx = peaks[0]  # 첫 번째 피크 선택
+        height = np.abs(Zxx[peak_idx, 0])  # 피크의 높이 추정
+        freq = f[peak_idx]  # 피크의 주파수 추정
+        amp = np.max(np.abs(data))  # 신호의 진폭 추정
+        progress = (peak_idx + HOP_SIZE) / RATE  # 충돌음의 진행 길이 추정
+
+        # 결과 출력
+        print("Height: {:.2f}, Frequency: {:.2f}, Amplitude: {:.2f}, Progress: {:.2f}".format(height, freq, amp, progress))
+
+    # 반환할 데이터 없음
+    return (in_data, pyaudio.paContinue)
+
+# 입력 스트림 열기
+stream = p.open(format=p.get_format_from_width(2),
+                channels=1,
+                rate=RATE,
+                input=True,
+                output=True,
+                frames_per_buffer=CHUNK,
+                stream_callback=process_audio
+                )
+
+# 스트림 시작
+stream.start_stream()
+
+# 프로그램 실행 중지 전까지 무한 대기
+while stream.is_active():
+    pass
+
+# 스트림과 PyAudio 객체 종료
+stream.stop_stream()
+stream.close()
+p.terminate()

+
+
+
+import sys
+import numpy as np
+import pyaudio
+
+RECORD_SECONDS = 5
+CHUNK = 128
+RATE = 44100
+DELAY = 0.1  # Delay time in seconds
+GAIN = 1  # Echo gain (0 to 1)
+
+# Create buffer for delayed audio data
+buffer_size = int(RATE * DELAY)
+buffer = np.zeros(buffer_size, dtype=np.int16)
+
+def add_echo(in_data, frame_count, time_info, status_flags):
+    global buffer
+    data = np.frombuffer(in_data, dtype=np.int16)
+    output = data + GAIN * buffer[:len(data)]
+    buffer = np.roll(buffer, len(data))
+    buffer[-len(data):] = data
+    return (output.astype(np.int16).tobytes(), pyaudio.paContinue)
+
+
+p = pyaudio.PyAudio()
+stream = p.open(format=p.get_format_from_width(2),
+                channels=1 if sys.platform == 'darwin' else 2,
+                rate=RATE,
+                input=True,
+                output=True,
+                frames_per_buffer=CHUNK,
+                stream_callback=add_echo
+                )
+
+print('* recording')
+
+stream.start_stream()
+
+while stream.is_active():
+    # Do other processing here if necessary
+    pass
+
+stream.stop_stream()
+stream.close()
+p.terminate()

+import base64
+
+task_list = []
+
+
+def display_menu():
+    print("일정 관리자")
+    print("1. 일정 추가")
+    print("2. 일정 보기")
+    print("3. 일정 완료 표시")
+    print("4. 종료")
+
+
+def add_task():
+    title = input("일정 제목 입력: ")
+    description = input("일정 설명 입력: ")
+    status = "하는 중"
+    task = { "title": title, "description": description, "status": status}
+    task_list.append(task)
+    print("일정이 추가되었습니다.")
+
+
+def view_tasks():
+    if not task_list:
+        print("일정 목록이 비어 있습니다.")
+    else:
+        print()
+        print("일정 목록:")
+        print("----------------")
+        for task in task_list:
+            print(f"제목: {task['title']}")
+            print(f"설명: {task['description']}")
+            print(f"상태: {task['status']}")
+            print("----------------")
+
+
+def mark_task_complete():
+    if not task_list:
+        print("일정 목록이 비어 있습니다.")
+        return
+
+    title = input("완료로 표시할 일정의 제목 입력: ")
+    for task in task_list:
+        if task['title'] == title:
+            task['status'] = "완료"
+            print("일정이 완료로 표시되었습니다.")
+            return
+
+    print("식별자와 일치하는 일정을 찾을 수 없습니다.")
+
+
+while True:
+    display_menu()
+    choice = input("선택: ")
+
+    if choice == "1":
+        add_task()
+    elif choice == "2":
+        view_tasks()
+    elif choice == "3":
+        mark_task_complete()
+    elif choice == "4":
+        print("프로그램을 종료합니다.")
+        break
+    else:
+        print("올바른 선택지를 입력하세요.")
+    print()
+

-import pyaudio
-from pydub import AudioSegment
-from pydub.effects import normalize
-
-# set up PyAudio
-pa = pyaudio.PyAudio()
-stream = pa.open(format=pyaudio.paInt16,
-                 channels=1,
-                 rate=44100,
-                 input=True,
-                 frames_per_buffer=1024)
-
-# record some audio from the microphone
-audio_data = []
-for i in range(0, int(44100 / 1024 * 5)):
-    data = stream.read(1024)
-    audio_data.append(data)
-
-# convert the audio data to a PyDub audio segment
-audio_segment = AudioSegment(
-    data=b''.join(audio_data),
-    sample_width=2,
-    frame_rate=44100,
-    channels=1
-)
-
-# apply an echo effect to the audio segment
-echoed_segment = normalize(audio_segment)
-
-# save the output audio file
-echoed_segment.export("output.mp3", format="mp3")
-
-# clean up
-stream.stop_stream()
-stream.close()
-pa.terminate()

+import sys
+import numpy as np
+import pyaudio
+import librosa
+
+RECORD_SECONDS = 5
+CHUNK = 1024
+RATE = 44100
+DELAY = 0.1  # Delay time in seconds
+GAIN = 1  # Echo gain (0 to 1)
+MAX_FREQ = 3000
+
+# Create buffer for delayed audio data
+buffer_size = int(RATE * DELAY)
+buffer = np.zeros(buffer_size, dtype=np.int16)
+
+def add_echo(in_data, frame_count, time_info, status_flags):
+    global buffer
+    data = np.frombuffer(in_data, dtype=np.int16)
+
+    def get_max_average_db(data):
+        data_float = data.astype(np.float32) 
+
+        # Compute the power spectrogram of the data
+        S = librosa.stft(data_float, n_fft=2048, hop_length=512)
+        S_power = np.abs(S)**2
+
+        # Convert power spectrogram to dB scale
+        S_dB = librosa.amplitude_to_db(S_power, ref=np.max)
+
+        # Calculate the average dB level
+        avg_dB = np.mean(S_dB)
+        max_dB = np.max(S_dB)
+
+        return avg_dB, max_dB
+
+    def get_dominant_freq(data):
+        data = data.astype(np.float32) / 32768.0
+
+        # Compute the Fourier transform of the data
+        fft_data = np.fft.fft(data)
+
+        # Compute the power spectral density of the data
+        psd_data = np.abs(fft_data)**2
+
+        # Define the frequency range of interest
+        freqs = np.fft.fftfreq(len(psd_data), d=1/RATE)
+
+        # Compute the power spectrogram on the mel scale
+        S = librosa.feature.melspectrogram(y=data, sr=RATE, n_fft=2048, hop_length=1024)
+
+        # Find the frequency bin with the maximum energy in each frame
+        max_bin = np.argmax(S, axis=0)
+
+        # Find the dominant frequency in each frame
+        dominant_freqs = freqs[max_bin]
+
+        # Compute the median of the dominant frequencies to get the overall dominant frequency
+        dominant_freq = np.median(dominant_freqs)
+
+        return dominant_freq
+
+    freq = get_dominant_freq(data)
+    avg_db, max_db = get_max_average_db(data)
+    print(int(freq), int(avg_db), int(max_db))
+    temp_gain = freq/MAX_FREQ
+    output = data + freq/2500 * buffer[:len(data)]
+    buffer = np.roll(buffer, len(data))
+    buffer[-len(data):] = data
+    return (output.astype(np.int16).tostring(), pyaudio.paContinue)
+
+
+p = pyaudio.PyAudio()
+stream = p.open(format=p.get_format_from_width(2),
+                channels=1 if sys.platform == 'darwin' else 2,
+                rate=RATE,
+                input=True,
+                output=True,
+                frames_per_buffer=CHUNK,
+                stream_callback=add_echo
+                )
+
+print('* recording')
+
+stream.start_stream()
+
+while stream.is_active():
+    # Do other processing here if necessary
+    pass
+
+stream.stop_stream()
+stream.close()
+p.terminate()

-import numpy as np
-import pyaudio
-import time
-
-pa = pyaudio.PyAudio()
-delay_buffer = np.zeros((44100, 1), dtype=np.float32)
-
-def callback(in_data, frame_count, time_info, status):
-    global delay_buffer
-    audio_data = np.frombuffer(in_data, dtype=np.float32).reshape(1024, 1)
-    delayed_data = np.concatenate((delay_buffer, audio_data))
-    delay_buffer = delayed_data[frame_count:]
-    return (audio_data + 0.5 * delay_buffer).tobytes(), pyaudio.paContinue
-
-stream = pa.open(format=pyaudio.paFloat32,
-                 channels=1,
-                 rate=1024,
-                 input=True,
-                 output=True,
-                 frames_per_buffer=44100,
-                 stream_callback=callback)
-start = time.time()
-DURATION = 30
-# keep the stream running for a few seconds
-while stream.is_active() and (time.time() - start) < DURATION:
-    time.sleep(0.1)
-
-stream.stop()
-stream.close()
-pa.terminate()
\ No newline at end of file

+import pyaudio
+import numpy as np
+import matplotlib.pyplot as plt
+import librosa
+import threading
+import sys
+
+print = sys.stdout.write
+
+
+# Define constants for audio parameters
+FORMAT = pyaudio.paFloat32
+CHANNELS = 1
+RATE = 44100
+FRAMES_PER_BUFFER = 1024
+DELAY = 0.1
+GAIN = 0.5
+
+# Open an audio stream
+stream = pyaudio.PyAudio().open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=FRAMES_PER_BUFFER)
+
+
+sound = []
+def get_stream_data():
+    global sound
+    sound.append(stream.read(FRAMES_PER_BUFFER*5, False))
+
+get_stream_data()
+
+
+# Create buffer for delayed audio data
+buffer_size = int(RATE * DELAY)
+buffer = np.zeros(buffer_size, dtype=np.int16)
+
+def add_echo(in_data, frame_count, time_info, status_flags):
+    global buffer
+    data = np.frombuffer(in_data, dtype=np.int16)
+    output = data + GAIN * buffer[:len(data)]
+    buffer = np.roll(buffer, len(data))
+    buffer[-len(data):] = data
+    return (output.astype(np.int16).tostring(), pyaudio.paContinue)
+
+
+
+def get_max_average_db():
+    global sound
+    data = sound[-1]
+    # Convert data to numpy array
+    data_float = np.frombuffer(data, dtype=np.float32)
+
+    # Compute the power spectrogram of the data
+    S = librosa.stft(data_float, n_fft=2048, hop_length=512)
+    S_power = np.abs(S)**2
+
+    # Convert power spectrogram to dB scale
+    S_dB = librosa.amplitude_to_db(S_power, ref=np.max)
+
+    # Calculate the average dB level
+    avg_dB = np.mean(S_dB)
+
+    print("Average dB: {:.2f}".format(avg_dB) + " "+ "Max dB: {:.2f}".format(np.max(S_dB)) + "\n")
+
+
+def print_dominant_freq():
+    global sound
+    data = sound[-1]
+
+    # Convert data to numpy array
+    data = np.frombuffer(data, dtype=np.float32)
+
+    # Compute the Fourier transform of the data
+    fft_data = np.fft.fft(data)
+
+    # Compute the power spectral density of the data
+    psd_data = np.abs(fft_data)**2
+
+    # Define the frequency range of interest
+    freqs = np.fft.fftfreq(len(psd_data), d=1/RATE)
+
+
+    # Compute the power spectrogram on the mel scale
+    S = librosa.feature.melspectrogram(y=data, sr=RATE, n_fft=2048, hop_length=1024, n_mels=512)
+
+    # Find the frequency bin with the maximum energy in each frame
+    max_bin = np.argmax(S, axis=0)
+
+    # Find the dominant frequency in each frame
+    dominant_freqs = freqs[max_bin]
+
+    # Compute the median of the dominant frequencies to get the overall dominant frequency
+    dominant_freq = np.median(dominant_freqs)
+
+    print("Dominant frequency: {:.2f} Hz\n".format(dominant_freq))
+
+
+threading.Thread(target=get_stream_data).start()
+
+while True:
+    get_data = threading.Thread(target=get_stream_data)
+    calc_data = threading.Thread(target=print_dominant_freq)
+    #get_decibel = threading.Thread(target=get_max_average_db)
+    
+    get_data.start()
+    calc_data.start()
+    #get_decibel.start()
+    
+    get_data.join()
+    
+    sys.stdin.flush()

+시나리오 작성 먼저 해보기. -> 때리는 소리따라 나오게, 
+시나리오에 따라서 딜레이를 줄이는 방식을 좀 더 만들어 보자.
+
+소리 왜곡방법은 알아서 재밌어 보이는걸로 해보자.
+지향성마이크도 한번 달아보기.
+
+1. 실제 소리변형으로 뭔가 만들기
+- 실제 소리가 들릴거냐 말거냐
+2. identification만 하고, 다른 소리 재생도 할 수 있음.
\ No newline at end of file

 import sys
 import sys
-
+import numpy as np
 import pyaudio
 import pyaudio
+import matplotlib.pyplot as plt
 
 
 RECORD_SECONDS = 5
 RECORD_SECONDS = 5
 CHUNK = 1024
 CHUNK = 1024
@@ -16,15 +17,54 @@ stream = p.open(format=p.get_format_from_width(2),
 
 
 print('* recording')
 print('* recording')
 
 
+# Initialize plot
+fig, ax = plt.subplots()
+x = np.arange(0, RECORD_SECONDS, CHUNK / RATE)
+line, = ax.plot(x, np.zeros(len(x)))
+
 def add_echo(data, output_stream):
 def add_echo(data, output_stream):
     output_stream.write(data)
     output_stream.write(data)
 
 
+# Initialize data arrays
+db_data = np.zeros(len(x))
+
 
 
 for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
 for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
-    add_echo(stream.read(CHUNK), stream)
+    byte_stream = stream.read(CHUNK)
+    data = np.frombuffer(byte_stream, dtype=np.int16)
+    fft_data = np.fft.fft(data)
+    # 주파수 대역 설정
+    freq_range = (200, 2000)  # 200Hz ~ 2kHz
+
+    # 주파수 스펙트럼에서 주파수 대역 추출
+    freq_spectrum = fft_data[(freq_range[0] // (RATE // len(data))) : (freq_range[1] // (RATE // len(data)))]
+
+    # 에너지 계산
+    energy = np.sum(np.abs(freq_spectrum)**2)
+    # 소리의 세기 계산
+    db = 10 * np.log10(energy)
 
 
+    # 소리의 높낮이 계산
+    max_freq = (np.argmax(np.abs(freq_spectrum)) * RATE) / len(data)
+    print("freq : ", max_freq)
+    print("db : ", db)
+
+    # Add data to arrays
+    db_data = np.roll(db_data, -1)
+    db_data[-1] = db
+
+    # Update plot
+    line.set_ydata(db_data)
+    ax.relim()
+    ax.autoscale_view()
+
+    plt.draw()
+    plt.pause(0.001)
 
 
 print('* done')
 print('* done')
 
 
 stream.close()
 stream.close()
 p.terminate()
 p.terminate()
+
+# Show plot
+plt.show()